Technical innovation is not separated from the social so much, but it is difficult to mix the two types of innovation. It is possible to arrange innovations along a continuum where, at one pole, social innovations are conditioned by technical innovations and, at the other, social innovations are first and autonomous. Social innovations (way of living together activities), in climbing or in diving, have been conditioned by the technical innovations of safety equipment. Social innovation, in this case, is consistent with the increased use of technology. But, social innovations can be more independent from technical innovations (adventure parks do not add new technologies to the equipment used in the army, but the ways of living together are different). Users become central in social innovation in outdoor sports. Involvement of the players is an essential condition for successful innovation. Keywords: outdoor sports; technical; technology; user La technique n'est pas séparée du social autant, mais il est délicat d'amalgamer les deux types d'innovation. Il est possible de ranger les innovations le long d'un continuum, où à un pôle les innovations sociales sont conditionnées par les innovations techniques et à l'autre où les innovations sociales sont premières et autonomes. En escalade ou en plongée sous-marine les innovations sociales (manière de vivre ensemble l'activité) ont été conditionnées par les innovations techniques du matériel de sécurité. L'innovation sociale dans ce cas va de pair avec l'intensification du recours à la technologie. Mais les innovations sociales peuvent être plus autonomes par rapport aux innovations techniques (les parcs aventures n'ajoutent pas de nouvelles technologies aux appareillages utilisés dans l'armée, mais les manières de vivre ensemble sont différentes). Les usagers prennent une place centrale dans l'innovation sociale dans les sports de nature. L'implication des acteurs est, dans ce cadre, une condition essentielle du succès des innovations.

Mots clés : sports d'extérieur; technique; technologie; usagers

During the twentieth century and until now, natural spaces have gradually become occupied by sports activities. Initially, when sports practices meant that humans were confronted with climatic extremes (deserts, mountain peaks), they were spaces for adventure. Later, they became spaces for competition; nowadays, much has been invested in nature in terms of leisure activities, and it is for these that it is now mostly used. Innovation did not belong specifically to any one of those periods (discoveryadventure, competition, recreation); on the contrary, it was part of each of them. Such *Corresponding author. Email: pascal.duret@univ-reunion.fr innovations empowered their inventors to overcome some adventure pursuit, and were subsequently used in a competitive spirit for comparing performances, and for sports in recreation activities, after they had been disseminated and mass-produced [START_REF] Duret | Le sport et ses affaires, Une sociologie de la justice de l'épreuve sportive[END_REF].

The significance of the social consequences of technical innovations in the area of outdoor sports was long minimized. Epistemologically, the overall issue of social innovation was mostly approached from the sociological perspective of organization or labor studies. In the 1980s, the main references on the subject, such as [START_REF] Crozier | À propos de l'innovation[END_REF] or [START_REF] Mintzberg | Structures et dynamiques des organisations[END_REF], construed innovation as a process of adaptation necessary for firms to survive. Innovation was opposed to routine and lack of flexibility, and thus was considered overflowing with redeeming adaptation. Those theoreticians viewed disorder as an essential resource for innovation and self-regulation. Such results, however, were applied to sports leisure activities only later. As a matter of fact, sports innovation was long viewed mostly from the perspective of performance improvement through technical transformation [START_REF] Vigarello | Techniques d'hier et d'aujourd'hui[END_REF]. The creation of research networks on outdoor sports (such as sportsnature.org) was a major step forward, when debates were launched on the issue of innovation in outdoor leisure activities [START_REF] Corneloup | Créativité et innovation dans les loisirs sportifs de nature[END_REF], but the debate is still quite recent.

The aim of this article is to distinguish the different patterns of links between technical and social innovations:

-Technical innovation is inseparable from intensified recourse to technology. The dérailleur in cycling, fiberglass poles in pole-vaulting, radial tires in Formula 1 and thermo-molded boot liners in Alpine skiing are examples of innovation, a word not interchangeable with invention. Technical innovation may be perceived as an invention now widely used. An invention is no more than a potential innovation, which still has to go through all the different stagesit has yet to prove that it is efficient, to be appropriated by a collective group, to meet the needs of a market, to be disseminated. In sports, the initial designer of a technical innovation is also its pioneer user. An invention is the initial artifact of a collective process [START_REF] Callon | L'innovation, un processus collectif ambigu[END_REF], and as [START_REF] Callon | L'innovation technologique et ses mythes[END_REF] suggests, it is necessary to discard any 'creationist' view of innovation [START_REF] Callon | L'innovation technologique et ses mythes[END_REF]. Technical innovation is always a 'socio-technical' innovation. Contrary to the positivistic belief in a strong determinism which would spur innovation, there is no such thing as natural evolution or inevitable progress toward technological advancement; you must go through a series of legitimacy conflicts, involving compromise, before you can reach an agreement over what innovation is all about [START_REF] Latour | Les microbes : guerre et paix[END_REF]. Therefore, it is essential to characterize innovation as one test, not to consider it as sufficient technological progress per se for it to be adopted. In view of this, the symmetry principle suggested by [START_REF] Bloor | Durkheim and Mauss revisited: Classification and the sociology of knowledge[END_REF] is necessary to understand why an innovation was accepted or why it was rejected. Thus, some technical inventions, despite undisputable proofs of their efficiency, have remained unused, due to the social resistance that they gave rise to; -Social innovation can be defined in the widest possible sense as an action aimed at meeting some social need by entailing strengthening of social ties; 1 -For example, several authors [START_REF] Charrier | Insertion par le sport : le choc des cultures. Sport, Europe, Stratégies[END_REF][START_REF] Duret | Sports de rue et insertion sociale[END_REF][START_REF] Falcoz | Intégration par le sport : representations et réalités[END_REF][START_REF] Gasparini | Le sport dans les quartiers : pratiques sociales et politiques publiques[END_REF] have shown how, in order to meet the social needs in underprivileged neighborhoods (especially to address violence), new sports systems were created. They gave new definitions for accessibility to sports, modes of transmission of sports knowledge and the organizing modalities for competitions, in such a framework as to foster selforganization. Such initiatives, when successful, make it possible to redefine social ties and increase solidarity, and, in that sense, are social innovation indeed.

Translation sociology has shown that social innovation, just like technical innovation, results from a dynamic process of controversies supported by networks. Consquently, the innovation process ends, as [START_REF] Akrich | Les objets techniques et leur utilisateur : de la conception à l'action[END_REF] suggests, when circulation of the novelty has ceased generating claims or controversies. The fact that the great majority has taken up the innovation stabilizes the way competencies are recognized in the old system and the new one. In that line of thought, social innovation implies a theory of agents in a network; what builds a 'social' group is an 'association' of agents, not their fossilized, statutory belonging to such-andsuch an organization.

Rather than listing the various levels of innovation, ranging from land development (from ski stations to parks), manufactured products (and transformation of equipment) and the new forms of sports practices (hybridization through combining innovation, such as 'kite surfing', a combination of surfing and kiting), to the forms of relations between practitioners (the change about the ties between them), we will try to focus on the social dimension of innovation, inasmuch as it produces identity disruption. Innovation about the use of sports equipment and services creates transformations in the commonly admitted characteristics and the legitimate ways of doing things in sports practices. We will identify several cases: first, when social innovation is dependent on a technical innovation (in which case it depends first on the invention of new sports materials or instruments, then on people making them their own); in the second case, technical innovation plays only a secondary role, and in the third, social innovation becomes independent from the technical component. The latter case usually consists in changes about the rules, which bring about social effects.

What is successful sports innovation?

Successful innovation is characterized by one 'invention', meeting the latent or formalized demand from a group. In fact, that is what makes the difference between successful innovation and failed innovation, which is bound to be perceived as some bizarre invention, an eccentric, useless oddity. Failed innovation is unable to find a following for itself, and above all cannot justify itself when criticized, or have an existence of its own. It may be an invention that comes too early (e.g., the back-up radar for cars, which measures the distance from an obstacle, was not selected to participate in the Lépine competition in the 1970s, but has been mass-produced for cars in the 2000s). An innovation is all the more fragile and vulnerable as it has no network, no group of fans; it remains an invention if it is not potentially supported by a market. As long as Steve Jobs stayed in the family garage to create personal computers, he remained a genius as an inventor; he really became an innovator only after he had founded 'Apple Inc.', with Steve Wozniak and Ronald Wayne, and managed to flood the market with Macintosh. Likewise, Petzl, a pothole explorer, was no more than an inventor as long as he was content with making the parts for his blockers and his lamps himself, in his own workshop. He became an innovator when he created a company to disseminate his lamps, not just for climbers but also for building workers. His son, P. Petzl, the current president of the company, says that his father 'for a long time worked out his ideas for his friends to use, making the tools which they needed to explore potholes' (Bouchet, Hillairet, & Lewi, 2009, p. 36). Chronologically, then, innovation comes after invention, which comes first (invention and innovation come together only in the case of manufactured products created on that concept). For Alter (2010, p. 18), 'the passage from invention to innovation is by no means immediate or linear'; the success of an innovation, according to that author, again lies in its capacity for wide dissemination.

In competitive sports, innovation enables the competitor who introduces it to pull ahead of the others. A new high jump technique, or the futuristic lines of a bike or a swimming suit, may help those who use them win a competition. As a result, the competitor who uses an innovation with a view to winning, far from wanting to disseminate it will just try to have it accepted into the current regulations of the competition.

To counter the lead taken by such individuals, the history of every sport is basically 'the history of the constitution of a set of rules' (Elias & Dunnig, 1986, p. 16), which makes it possible to preserve equality between the competitors. The evolution of sports regulations is related to the need to preserve equal chances from the start, without which it would be impossible to establish any hierarchy between the merits of each competitor at the end of the competition. Either an innovation is banned if it is not reproducible, or it is admitted if it is easily reproducible by all; this puts an end to the lead taken by the innovator, who is then bound to find new innovations [START_REF] Duret | Le sport et ses affaires, Une sociologie de la justice de l'épreuve sportive[END_REF].

The borders between technical and social innovation are so permeable that they cannot be considered as two separate areas. The technical aspect is not separate from the social one. However, the two types of innovation can hardly be amalgamated. Taking the factor for innovation as the criterion for grading, it is possible to place several models along a continuum, with social innovations conditioned by technical innovations at one end, and social innovations predominating at the other.

When social innovations are conditioned by technical innovations

Several authors have described the effects of technical innovation on sports leisure activities [START_REF] Loret | Géneration glisse : dans l'eau, l'air, la neige … la révolution du sport des "années fun[END_REF]. In 'Génération glisse' (1995), Loret suggests that successful innovation implies several ingredients. First of all, it is necessary to create new objects (surfing and windsurfing boards, snowboards, etc.), which change the way sports people perceive their activity, and make it possible for them to shift from one culture to another (i.e., the passage from traditional competition to dissemination of the 'fun culture'). In this model, technical innovation enables cultural innovation to break forth. Technical innovations are frames of reference that foster cultural transformation. As [START_REF] Pociello | Les cultures sportives[END_REF] has shown, the 'purists' in a discipline will be more reluctant about using an innovation than newcomers will. For example, in snowboarding, when traditional ski boots are discarded in favor of soft boots, it is not only free-ride techniques which are revolutionized; new forms of relations are made possible between snowboarders, who are, henceforth, freer in their movements, and less directly dependent on ski lifts.

Among the many cases which could illustrate the role of innovation in the historical evolution of sports, alpinism and climbing are most striking. A social history of alpinism cannot be separated from the evolution of the material made available to sportspeople. The attention paid to small technical objects which are easily categorized as trifles [START_REF] Piette | Ethnographie de l'action[END_REF] is essential to understand the major changes in sports disciplines. For example, it is by describing how climbers have used one type of karabiner after another, or were offered new possibilities (when karabiners were made lighter, and later were fitted with new unlocking systems), that you realize how those new possibilities have influenced sportspeople's togetherness. In alpinism, climbing or potholing, the fact that downhillers are now acting as protectors too has changed not only individuals' relationship to safety, but the relationships between practitioners. For example, the early alpinists were physically linked to their partners by the rubbing of the S-shaped rope on their thighs, back and shoulders, so that being careful of the others also meant being directly careful for yourself. The roped party was crucial for solidarity. Roping yourself up then meant 'linking your fate to someone else's, for better and sometimes for worse; it was both a physical and a symbolical attachment' (Boutroy, 2011, p. 40). That extreme form of attachment encompasses, in the climbers' view, the need for mutual help and solidarity, interdependence in an adverse context, as well as the distribution of tasks, with a view to reaching one common objective, i.e., the summit. Once belay devices became available (such as Sticht plates in the 1970s), each climber became more autonomous; solidarity was still necessary, but one climber could be less dependent on another's speed. After they had begun using those small plates, every member of the climbing party could indulge in thinking that they no longer really needed the others, or, at any rate, that they no longer had to put their lives into other peoples' hands. The group of alpinists becomes more competitive, as members each believe they are a team by themselves, and thus are led to feel almighty. That trend was reinforced by the marketing of the ATC Guide (Air Traffic Controller) plates, which can be directly set up on a relay point so that the rope can tighten up in the event of a fall (automatic safety for the second climber in the team). Lastly, thanks to the new self-locking belay systems (of the Gri-gri, Cinch or Eddy type), it is now less vital for climbers to constantly take care of one another. As [START_REF] Cassirer | An essay on man: An introduction to the philosophy of human culture[END_REF] wrote, humans are symbolic animals; as a matter of fact, the technical transformation of one real fact (through adoption of a new type of material) always carries with it an underlying symbolic dimension. When, in a climbing team, belay is no longer ensured by a human, but by pitons, the symbolism of the fall is revolutionized. Likewise, once alpinism has been superseded by free climbing in France [START_REF] Aubel | L'escalade libre en France[END_REF][START_REF] De Lezeleuc | Comment peut-on enseigner une culture corporelle? Tréma[END_REF], falling no longer suggests death quite so directly; it becomes part of the practice, which now includes aesthetic constraints. Sometimes, it is even thought of as a good laugh. This anecdote reported by de Leseleuc shows how what was formerly a tragedy could be turned into a joke:

The other day, I think they were both Parisians. One was a very good climber; he had been to the Claret before. The other had tried to play 'king of bongo' and failed, so he came down again. . .. The better one went and fixed the karabiners, then he came to the fake point and he said . . . We were below, we were laughing because we didn't know he had been on that track before. The guy looked at us and said: 'Stupid you, why did you have one more piton? It was better as it was before'. Then he saw the piton (the fake one), and he said: 'Oh, fuck'. He had got it, but he said nothing. He went up, and fixed all the karabiners for his pal. His pal rushed up the track (laughs), and there he was, it was a cardboard karabiner (laughs)cardboard, yeah??? You can imagine, Hubert could not help it; he was laughing his head off (laughs). So his pal had been in the joke all along; he had seen the fake karabiner but he said nothing'. (De Lezeleuc, 1997, p. 21) Not all new technical devices that are introduced to increase climbers' protection and safety are necessarily accepted. Social resistance to innovation, among 'purists', is due to alleged depreciation of safety rules.

Another striking example of how technical innovations trigger off social innovation is provided by the evolution of deep-sea diving equipment. Whereas diving suits used to be linked to the boat and the pump which supplied them with air, the autonomous bottles fitted with regulators, developed by Gagnan and Cousteau in 1943, have made the equipment much lighter and more manageable in size. This has turned diving from an extremely risky practice into a leisure one. Deep-sea divers were people with extraordinary courage, daredevils whose love for the seabed could not reasonably be taken up as a model reproducible for ordinary people. Unlike them, divers who use bottles (patented and trademarked 'aqualungs' by Cousteau in 1945) can be viewed as leisure figures. The successive generations of regulators (Mistral, Spiro & Neuchat), as well as stabilizing jackets, are making diving increasingly accessible. The diving team is a metaphor for solidarity between divers as well as solidarity between them and nature. Here is the same figural contrast between an individual who is isolated, though dependent on others (the deep-sea diver who is alone in the depth of the sea but is kept alive by the 'umbilical cord' which links him to the air pump on the boat), and the group, which is convivial precisely because of the solidarity among all the members (the deep-sea divers), though they are not dependent on one another. No wonder the 'Club Méditerranée' made diving one of its key activities in the 1970s; the idea was to discover the seabed in a merry, recreational way, thanks to technical progress.

One more example of the social effects of a technical innovation is provided by the shift from the traditional mountain bike to the double suspension, and later the 'free-ride' (wholly suspended) bikes. That technical innovation transformed the practice, which from a predominantly energy sport became a gliding one, in which balance and sensations are the primary aim. Thanks to increased clearance of the fork, shocks are better absorbed, leaving the mountain bikers free to ride on steeper, more jolting tracks. But the idea, rather than the ability to toil up the steepest lanes, is now to race down the steepest slopes. Sportspeople are younger, as the evolution of the ages among the members of the federation shows.

When social innovation prevails

A second comprehensive type of innovation is social innovations which do not directly depend on technical innovations, but address a social issue. For instance, 'shark alert' security systems were established by surfers on Reunion Island after an outbreak of shark attacks. Since 2011, due to more frequent shark attacks, beaches have been gradually closed off, and access to the waves ('spots') forbidden. To make up for a lack of public management of the shark risk, other than bluntly creating a ban on surfing, sportspeople concerned with the risk, and those in charge of raising the alarm, organized themselves with a view to ensuring safe practice of the sport. The system they established is a sports innovation as the surfers, to ensure their own safety, gave up a traditional practice (wave riding) by 'taking over' the stream below the waves. Here is division of labor, with one surfer on the board being accompanied by another who is swimming without an aqualung below the water to prevent a possible shark attack. New physical dimensions (getting familiar with the environment underwater, diving without an aqualung) as well as symbolic ones (immersion increases the symbolic presence of the risk) are added to the ordinary practice of surfing. This is social innovation inasmuch as the adjustment entails a new distribution of tasks, or even a covenant with divers. Here is indeed 'unexpected association' (Alter, 2010, p. 73). For a social innovation to be understood, it must be framed in its social contextin this case, the context of the 'shark crisis' [START_REF] Morel | Le risque requin, mise en risque de la pratique du surf à la Réunion[END_REF] so that the constraints it has taken into account and the normative system from which it originated, or against which it was deployed, can be identified. The new way of surfing in pairs (a surfer/an underwater watchman) has emerged in a political context characterized by contrasted, competing stances ranging from denial and blurring out the risk to over-dramatizing and exaggerating it. One major consequence of that wide gap between the different policy stances is that it leads to inaction on the part of decision makers. Surfers are all the more at liberty to innovate as they can use the space that public management of the shark risk has left vacant. They have positioned themselves by opposing the institutions (the marine reserve, scientific research organizations), which they describe as impotent in addressing the crisis. They even consider that such institutions are the sources of the attacks, as the conservation of fish species has created an attractive food reserve for the increasing population of sharks. They are seeking to innovate as far as they can, considering that the risk taking involves only their responsibility as surfers.

The overall creative process is usually the result of several factors or entities combining to usher in the shift from one configuration to another (Hillairet, 2005). A field survey by Thiann-bo (observation of three shark-alert systems, monitoring and interviewing 23 surfers, monitoring the Facebook virtual community) led to a better understanding of the surfers' reasons for innovating [START_REF] Morel | Le risque requin, mise en risque de la pratique du surf à la Réunion[END_REF]. Not all sports people have the same reasons for innovating. Some advocate enhanced responsibility and sustainable change among individual surfers. Others have set up an association to gain influence over the public management of the risk, and believe that the present shark-alert systems should be no more than an 'additional' remedy within a larger safety system (intensive shark fishing, frightening the sharks off). The systems they imagine also depend on their own relationship to nature and to the animals. The reasons for innovating vary depending on the animal mythologies mentioned by the surfers. The more harmful sharks are perceived to be, the more superfluous, or even irrelevant, the question of respecting them becomes.

As it turns out, the following were the characteristics necessary for that social innovation: the lack of public policies, rising awareness among the various users of the ocean (with some usually identifying themselves by rejecting the others) and the building of solidarity. The surfers ask people from other water disciplines, during a competition for instance, to help them make their surfing sites safe. For as long as one session lasts, the shark-alert people stop being surfers; they become 'watchmen' in charge of keeping watch on sharks underwater. There are different types of shark-alert peopleon a boat, underwater or on land (the latter are also known as 'shark spotters'). The shark-alert system is an innovation in usage, which consists in engaging individual surfers in more activities: during a session, they become divers, swimmers and surfers in turn. They get organized in an autonomous way, broadcasting the best weather conditions for practice and setting up groups with enough members to ensure safe surfing.

Steps are taken to make the exchange of positions possible. In this model, the social innovation is supported by technological innovations, such as Smartphone apps to build synergy between the actors.

When social innovations become independent from technical ones

Some social innovations do not need any technical support at all to develop, and are, therefore, of the solely social type. For example, one activity may simply move from one public to another. For instance, the paths in adventure parks are more or less those laid out by commando training centers (particularly the Montlouis Center). The equipment for military training (Tyroleans, pendulums, rope bridges, etc.) was not technically improved when it was imported into adventure and leisure sites; the innovation concerns solely the change of public and the way physical testing is conducted. Whereas in commando training centers, instructors usually get the trainees to work the rough way, adventure site coordinators tell tourists: 'You go ahead when you like, when you feel like it'. The obstacle will be overcome in both cases, but whereas in the former it is under duress, in the latter the decision to start the action is left to the user.

A good example of this type of social innovation is provided by raids and extreme races such as sand marathons or the Réunion Grand Raid, in which the social changes are derived solely from innovations about the rules. The latter do not conform to the 'creative destruction' process which [START_REF] Schumpeter | The theory of economic development[END_REF] described in the early twentieth century. New modalities are simply superposed on old ones, which are not erased. There is no revolution in the norms, just the coexistence of several norms. In addition, the innovation is recognized as such by the participants. One essential effect of the innovation in such races is that individuals are free to define the type of relationship they want to have with the others. The racers fashion their races as they like. The races are innovative in that they leave the participants free to choose between different valuescompetition, discovery and care for others. Here is a model of sports in which competition for the best ranking coexists with good-humored shunning of any 'win at all costs' spirit. It has done away with the seemingly irreconcilable rift between ruthless sport clashes and leisure-discovery activities. In this case, it is up to everyone to choose their own mix of both trends. Everyone is free to shape their own conception of the race, and eventually their conception of themselves. These are infinitely variable exercises which, far from submitting to inflexible prescriptions of uniform types of conduct, authorize or even encourage participants to engage in a wide range of personal projects. Downright competition has not been swept away by new forms of softer, convivial competition. The old modalities have not been declared obsolete to make room for new ones. In fact, the innovation lies precisely in tolerant coexistence of the old and the new. The social innovation lies in turning away from uniform projects and behaviors. That tolerance to diversity is what cements unity among the racers. Participants do not claim they belong to any particular community or sports fraternity (which would imply indissoluble ties), but rather welcome strangers benevolently. Coming to participate in the 'Grand Raid' or other trails does not make participants 'brothers' or members of one family. Each participant comes in a private capacity. Relationships during a race are all the more intense as, paradoxically, they do not involve any lasting community. The ties are more friendly (Tonnies call them 'members') than brotherly ('community'). Relationships between sportspeople have usually been glorified, especially by the thinkers who started the sports movement, as brotherlywhich implies continuity of a kind (you cannot say: 'You're no longer my brother', whereas you can say: 'You're no longer my friend'). Sports brotherhood is historically based on patriotic values which exalt common belonging (whether to a town, a region or a nation). On the contrary, friendship implies an elective choice of 'individualized individuals' [START_REF] Singly | Les uns avec les autres, quand l'individualisme crée du lien[END_REF], tracing an ideal tie based on liberty and conviviality. The project consists in giving up all ideas of uniform commitments and conducts. This is akin to the stance taken by Julia [START_REF] Kristeva | Étrangers à nous-mêmes[END_REF], who claimed that the world should be viewed as entirely made up of strangers. If everyone is a stranger to the others, then nobody is, since if there is no identity to which you belong, then there is no possibility to discriminate otherness. The cost for that type of association is that the ties are very short-lived. This form of raid is all the more socially innovative as those new forms of ties between 'strangers' who did not know one another before the race began are superposed on traditional, long-standing sports friendship.

The identity disruption suggested by that type of race implies the emergence of a 'we' not a 'multiplied I' (in which each competitor has the same project as their neighbor), but a 'dilated I' (in which the identity profile of the race is provided by the sum of the various projects of the competitors). In the former case, that of traditional marathons, 'we' is based on a single project; in the latter, that of the Réunion Grand Raid, everyone may join in with their own project. Such emancipation from a single project implies that everyone accepts and more or less identifies with their neighbor's project, even though it is not their own; in that collective identity scheme, individuals may find themselves outside themselves. That type of race is a metaphor for common humanity. The shift from 'me' to 'us' does not necessarily mean that the image which individuals have of their own activity is strengthened; rather, it is a way of accepting through 'us' some sports modalities which are different from our own. Individuals thus assert themselves in the human condition, by simultaneously claiming their right to be different, a multicultural sports event and inseparably, their own individuation. The group, far from making the participants uniformly similar, enables them to hold a position which provides their subjectivity with visibility. The idea is to think of others as both different and equal, an innovation as far as social ties are concerned. For the thinkers of the right to be different, such as Louis Dumont, the only way to escape the relativism of an 'equal status' society or group is to establish hierarchy. Consequently, in the view of the author of Homo hierarchicus and Essais sur l'individualisme, the only way otherness can be recognized is by conferring on it a value lower or higher than the one you confer on yourself. Louis [START_REF] Dumont | Essais sur l'individualisme[END_REF] is adamant that 'only hierarchy can ensure recognition; I maintain that when the advocates of difference claim both for equality and recognition, they are claiming for the impossible ' (1983, p. 260).

Conclusion

In the case of technical innovation, a small group of innovators, or even a single person, initially tries to address an individual issue, whereas, from the start, a social innovation is intended to address a social issue. Reality concerning the process of technical innovation certainly belies the myth of the 'genius handyman' who would also turn out to be a genius entrepreneur as well as a genius communicator and a genius negotiator. . . It is not so easy to be outstandingly good in every area (and even if that were true, it would still be very difficult to move from one area to another); that is the reason inventors need specific allies if they are to control at least part of the innovation process. As a result, in the realm of innovation, sociologists are usually faced with the hen/egg paradox. Is successful innovation related to the characteristics of the product, or rather to the properties of the receiving market [START_REF] Mendras | Le changement social[END_REF]? To overcome the dilemma, translation sociologists cancel out the terms of the alternative: in their view, the social environment is built simultaneously with the innovation it will assess [START_REF] Akrich | Sociologie de la traduction. Textes fondateurs[END_REF]. Consequently, it is necessary to envisage joint building of the innovation and of the attendant social order.

Users hold a central position in social innovation as far as outdoor sports are concerned. Representation of sportspeople by proxy (through experts, elected representatives or managers) can hardly be enough for this type of innovation. In such a framework, involvement of the players is an essential condition for successful innovation. It is the users themselves who make the devices their own, transforming and stabilizing them in the process. The userinnovator will find ways of pushing back the limits of devices. It thus appears that in outdoor sports, the division of labor between those who design the innovation and those who are supposed just to use it is sometimes far from distinguishable.

Social innovation in outdoor sports nowadays consists in refraining from defining a priori a single legitimate form of social ties; far from that, it consists in enabling a variety of norms to come out. In addition, it offers a compromise between individuals' claims for ever-increasing autonomy and their wish for continued relationship with their friends. Social innovation in outdoor sports makes it possible for individuals both to assert themselves as such and to meet the expectations of collective living.

  Two norms for innovation in outdoor sports: Technical and social innovation Pascal Duret* a and Katia Angué b a Université de la Réunion, Laboratoire DIMPS, Campus universitaire du Tampon 117 rue du Général Ailleret 97430, Le Tampon, France; b MCF, Université de la Réunion, Laboratoire CEMOI, Faculté de Droit et d'Economie 15, avenue René Cassin -CS 92003, 97744 Saint-Denis Cedex 9, France

Disclosure statement

No potential conflict of interest was reported by the authors.

Note

1. Theoretically, the will to delete the tie could also be an object for innovation, but actually it is cohesion and reinforcement of the tie which are aimed at through the facilities intended for social innovation.


Cyril Buttay
email: cyril.buttay@insa-lyon.fr

Olivier Brevet

Bruno Allard

Dominique Bergogne

Hervé Morel

Morel Cegely

Paralleling of Low-Voltage MOSFETs Operating in Avalanche Conditions

Keywords: Automotive component, automotive electronics, device modelling, MOSFET, thermal design

come   L'archive ouverte pluridisciplinaire

Avalanche in automotive environment

It has been stated above that avalanche operation is caused by the small margin between automotive operating voltage (typically 14 V) and the bireakdown voltage of the transistors (20 to 25 V). In order to keep converter costs low, filtering capacitors are reduced to the lowest value possible. Therefore, the inductive behavior of the wires connecting the converter to the vehicle battery is no longer masked by these capacitances. This results in a voltage spike across the transistors during turn-off. Another cause of avalanche operation, more specific to the automotive environment, is the load- dump [START_REF] Namuduri | Load dump transient control of a 42v automotive generator[END_REF], [START_REF] Efland | The load duwnp[END_REF], which is a spurious battery disconnection while it is being charged by the alternator. If this occurs, there is no more device to limit the vehicle network voltage and absorb the current generated by the altemator. Therefore, the alternator magnetic energy has to be dissipated by other means in order to avoid a voltage rise that could exceed 100 V.

That regulating role is fulfilled by the MOSFET inverter (or by the diode rectifier bridge in classical alternator applications). Due to instability of the low-voltage MOSFET transistors when in saturation mode [6], it is not possible to use them for linear regulation. Therefore, the only way to clamp the network voltage is to use the transistors in avalanche operation. 2

Low voltage MOSFET structures

Power MOSFET transistors are very well suited to low voltage applications as their low on-resistance creates lower voltage drop than with IGBTs.

RDSOTh value is the key parameter of these high-current devices (cutrent ratings over 100 A in a D2PAK package are not uncommon). Several parameters are mrsponsible for the RDSG, value of a MOSFET (epitaxial layer resistance, JFET effect,... [START_REF] Baliga | Pow"er semiconductor devices[END_REF]), but for low-voltage devices, channel resistance and packaging (bond-wires and connections) are the most important.

Channel resistance is proportional to the MOSFET channel width. Several technologies are used to increase this width in a constant transistor area (i.e increase the channel density): strip layout, trench gate [START_REF] Baliga | Pow"er semiconductor devices[END_REF], [7]... Measurements made in this paper have mainly been carried out on STMicrOelectroItics STripFET STB210NFO2 transistors. These MOSFETs have been described in literature [START_REF] Belverde | Advanced characterization of low-voltage power mosfets in synchronous-rectifier buck-convelter applications[END_REF]- [START_REF] Musumeci | Low-voltage mosfets with improved performances in advanced dc-dc converter applications[END_REF]. They use planar channel technology (i.e non trench) with a strip layout that allows the increase of channel density by "simplifying the silicon fabrication steps" [START_REF] Shenai | Modelling low-voltage power mosfets as synchronous rectifies in buck converter applications[END_REF].

Measurement setup

The measurement setup that is used to identify the avalanche model parameters has been described in details in [START_REF] Buttay | Avalanche behavior of low-voltage power mosfet[END_REF]. The principle is to measure the transistor temperature just after the avalanche pulse by the means of a thermal sensitive parameter: the body diode forward voltage drop. Successive measurements are performed with increasing avalanche pulse dturation. It is then possible to build the temperature evolution by superposing all the measurements. The schematic of this system is given in figure l(a). 3.1 Principle and schedule of a measurement iteration Driving waveforms are pictured in figure l(b). At the beginning of a measurement cycle (stage 1), the voltage across the capacitance is v0, and current in the inductor is zero. During the second stage, inductor L is charged by closing switch I, maling vt decrease down to zero. At this moment (stage 3), current in L is maximum and ] is opened ((X and (©) were closed before). Therefore current flows through [START_REF] Chen | Analysis of avalanche behaviour for paralleled mosfets[END_REF], which is the Device Under Test. Some microseconds later (short enough for self- heating to be negligible) DUT is opened, triggering avalanche process. As I has higher VBR rating, breakdown occurs in the DUT (phase 4). After an arbitrary delay, ® is closed once again to divert inductor current, then © is opened to isolate the DUT (phase 5). Temperature is obtained by biasing the intrinsic body diode of the MOSFET by a low-value measuring current and acquiring the voltage drop VF [START_REF] Blackburn | Power mosfet temperature measurements[END_REF].

Measurements exploitation

This process is repeated several times, with increased avalanche pulse duration (this corTesponds to an increase of stage 4 duration in figure l(b)). Measurements acquired during each iteration are then superimposed (figure 2). Drain current and drain to source voltage during avalanche are plotted respectively in figures 2(a) and 2(b). Their "sliced look' is due to the superposition of successive acquisitions with increasing avalanche duration. At the end of each acquisition, body diode forwaild voltage is recorded, and plotted in figure 2(c). Using the linear relationship between forward-biased voltage drop of a diode and its temperature that was obtained through previous calibration (the Vip = f (T) is shown in figure 3). it is then possible to plot temperatLre evolution of the MOSFET during and after avalanche (figure 2(d)).

A simple electrothermal model

An interesting fact is that peak temperatLre is reached at t = 400 Its (see figuLre 2(d)), while peak drain to source voltage is around t = 150 /Ls (figure 2(b)). This denotes that VDS evolution is not curent and the drain-to-source voltage of the MOSFET under test during avalanche, (c) is the drain-to-source voltage of the MOSFET under test after avalanche with forward biasing of its body diode (this colTesponds to a close-up view of (b)).

As the forward voltage drop of a diode is linearly dependent on its temperature, it is possible to plot the temperature profile duwing avalanche (d) from (c). only related to the die temperature. Furthermore. VDS is higher at the beginning of the current pulse (where chip temperatLre is as low as 30°C. and drain current is more than 100 A) than at the end (temperatuLre is then almost 100°C, but current is near zero A). This demonstrates a resistive behavior during avalanche. as breakdown voltage is dependent on the avalanche current.

Breakdown voltage model

A very simple model describing drain to source voltage as a function of drain current and chip temperatuLre is as follows: VBR(T) = VB& +± ,(T + RBRID

(1) This expresses the linear dependence of the breakdown voltage VBR with temperature and with drain culTent. T is the transistor temperature (in Celsius degrees), V7m3R is the 0°C breakdown voltage, and ,B reflects temperature dependence of V1BR (V.0C-1). RBR is the avalanche path resistance (S).

The paramiieter set is chosen as to ininimize (least square criterion):

N Z (VDSi -BR, -,3T -RBR_D)2 (2) i=o)
Where VDS;g, T, and ID, are respectively the discretized i6l' point of the drain-to-source, transistor temperature and drain current waveforms. An interpolation algorithm is used to compute the value of Ti between the points of figure 2(d), The results of the identification process are as follow * 1BR, = 26.9 V; * 3-14.8 rnVOC-I; * RBR = 12 mnQ, 4 times bigger than the RDSQ, of the MOSFET (STMicroelectronics STB21ONFO2,

mQ).

A comparison between the measured drain-to-source voltage and the corresponding waveform computed using equation (1) fed with measured drain current and transistor temperature shows a good agreement (see figure 4).

Thermal model

In order to perform electrothermal avalanche simulations, it is necessary to add a thermal model to the electrical model proposed above. This thermal model allows for transistor temperature calculation from the dissipated power waveform.

ISBN 90-781S=08-5 TJ P kIT Fig. 5. Thlleral model based on an electrical-equivalent network, The simulations pr:esented in this paper were performned using t00 RTH -CTH -cells as a thermal model for the silicon die. A classical thermal RTH, CTH network (see figure 5), based on unidimensional finite differences discretization is calculated [START_REF] Hefner | Thermal component models for electrothermal network sinulation[END_REF] using the following relationship:

T RTHB =JKA (3)

C CTH = hApe

Where A is the tarnsistor die area (m2), /t the discretization width (m) and K, p and c are respectively the silicon thermal conductivity (Wm-m.K-'), density (g.m-3) and specific heat (J.g-'.K-'i To achieve better accuracy over several hundreds of microseconds, the copper heat spreader (located under the silicon die) must also be taken into account, also using equation set [START_REF] Chen | Analysis of avalanche behaviour for paralleled mosfets[END_REF]. Finite-differences-based thermal modeling is not the most efficient [START_REF] Anmnous | Choosing a thennal model for electrothennal simulation of power semiconductor devices[END_REF], but providing enough subdivisions are considered, it is accurate for short power ptulses (some microseconds).

Finally, some empirical modifications are mandatory to take into account the source metalization and bond-wires: at the beginning of heating, the heat flux is not unidirectional, as surface metallization (several microns thick) and bond-wires (up to 500 p.m in diameter) represent an extra thermal capacitance, equivalent to about 40 jtm of silicon above the active area. It has been experimentally verified both on encapsulated and bare-die transistor that epoxy encapsulation has negligible influence on thermal transients during avalanche.

The whole electrothermal model is therefore obtained by adding the electrical model of eq. ( 1) and the thermal network (figure 5). as depicted in figure 6. As this model is implemented into Pspice. which does only allow for electrical modeling, the thermal model is represented using electrical components such as resistance, capacitances, amd current (representing power) and voltage (equivaldent to temperature) soLrces.

Figure 7 shows a comparison between chip temperatu and drain to souire voltage obtained by the previous measurements and by simulation using the electrothermal model depicted in figure 5. 100 subdivisions have been considered in the silicon die thickness, and 30 for the copper heat spreader.

It can be seen that the thermal model is valid up to 300 pts after the beginning of the avalanche pu;lse, but over that time, it is no longer accurate, as heat flow in the copper heat spreader is no more unidimensional. 5 Application to simulation of MOSFET paralleling No previous work addressing MOSFET paralleling during avalanche talkng into both account temperattre and drain current influence was found in liteatutre. In [START_REF] Reirmuth | The ruggedness ofparalleled power mosfets[END_REF], authors only take fRR 0|V L 95~~C Di iD2 iD3 D Fig. 8. Electrical schematic used for simulation. Switch S is tned-on to charge the inductor L, and then turned-off. This results in avalanche operation of the body diodes (DI to N4) of the foLu paallel MOSFET transistors. The electrothermal avalanche model of the diodes is depicted in figure 6.

into account, while authors of [3] only consider thermal effects. In this section, simulation will be perforned to estimate the influence of VBi, dispersions on current sharing during avalanche.

As explained in section 1.1, breakdown voltage mismatches between paralleled transistors can have a strong effect on reliability, as they can result in a high thermal cycling of one of the transistors. As experiments are difficult to carry out when addressing transistors manufacturing dispersions (this would require to have a transistor set that is representative of these dispersions), simulation is well suited to analyze the influence of VDR mismatches.

The schematic of figure 8 has been used to simulate the behavior of unmatched transistor paralleling. An inductor is charged by turning switch S on. When S is turned-off. voltage rises across the diodes DI to D4 until it reaches the breakdown voltage of one of the diodes. Then avalanche phenomenon begins, and lasts until the inductor is totally discharged.

Using the four-paralleled devices system of figure 8, with VTB3R0 values ranging from 255 V to 27 V (evenly spaced), two simulation runs have been carried out with varying JBgR value. In the first case. only the wiring resistances are taken into account for R.UR (bond-wires, transistor connections...) and are estimated to I mQ. In the second case, the value of R3p obtained through the identification process described in section 4.1 (12 m&2) is considered. Results are plotted in figure 9. Figure 9(a) shows the drain cLrrents in both cases and figure 9(b) the corresponding temperatLre waveforms.

In the first case, RsR is low, so it has almost negligible influence on current sharing. At the beginning of the avalanche pulse, the transistor with the lowest VBR (25.5 V) has to sustain all the current flowing through L. Its temperature rises quickly, making its breakdown voltage increase up to the second lowest VB n, value (26 V). Then avalanche current flows through two transistors, but further increase in the breakdown voltage will not be large enough to reach VsB0 of the third trmsistor. This results in large self-heating of the firt transistor (550C above ambient). In the second case, R1BR is much higher (12 mS2). As VBp mismatches are set to 0.5 V between two transistors, the maximum resulting current unbalance is I = 0.5 = 41 A. It can be seen on figure 9(a) that this results in better current sharing between all transistors. as they all conduct as soon as the switch S is tLrned-off. Maximum temperature rise is then 35°C above ambient (figure 9(b)).

The positive influence of the avalanche path resistance on current sharing is demonstrated. Because of its high value (in the order of four times the RDS,,), it is responsible for voltage drop comparable to the VBR mismatches between transistors, and therefore acts as a feedback to minimize current tnbalance. The influence of temperature is also non negligible, as 55 °C increase in temperature involves a 0.8 V increase of the breakdown voltage of trnsistors. Simulations performed with the classical avalanche model (only temperatLre dependent) yield to a 55 °C temperature rise, far more than the results obtained using the proposed model (35 °C). As this latter model has proven to be accurate (see the experimental validation of figure 7), one can expect

hal-00138894, version 1 -28 Mar 2007

IanoudlingZvZRa.e,WiFwo10S WOrASah(ediu B=sTAYQC"il a real temperature increase lower than that estmated using the classical model. In the design stage, this translates into wider acceptable limits on V17R dispersions, and maybe no necessary screening on this parameter. This in turn reduces costs.

Conclusion

The interest of electrothermaL avalanche simulation when transistors operate in paraLlel has been demonstrated. Breakdown voltage mismatches between transistors can result in stressing one of the transistors of the assembly.

The proposed electrical model has a very simple expression. Its parameters are experimentally obtained using a dedicated test setup. The thermal model is obtained through calculation. The com- bination of these two models shows good accuracy.

Using this electrothermal model, simulations are performed to estimate the influence of breakdown voltage mismatches on transistors temperature. It is shown that temperature increase is over-estimated using classical models in comparison to the experimentally verified proposed model. This is due to the balancing effect of a specific avalanche path resistance. Failure to consider this resistance in the avalanche simulations yields to about 40 % error in the computed temperature rise.

Fig. 1 .

 1 Fig. 1. (a) test setup allowing temperature profile measurement and (b) corresponding waveforms.

Fig. 2 .

 2 Fig. 2. Figures (a) to (c) axe measurements pefformed using the proposed test setup: (a) and (b) are respectively the drain

Fig. 3 .Fig. 4 .

 34 Fig.3. Calibration curve of the temperature-sensitive parameter The forward voltage drop of under a 0.6 A bias cunrent, decreases linearly with increasing temperature.the MOSFET body diode,

Fig. 6 .

 6 Fig. 6. Electrothennal model of avalanche

Fig. 7 .

 7 Fig. 7. Comparison between simulated and experimentl drain-to-source voltage and transistor temperature.

Fig. 9 .

 9 Fig.9. simulated drain current(a) mismatclles.


Amel Tuama
email: amel.tuama@lirmm.fr

Frédéric Comby
email: comby@lirmm.fr

Marc Chaumont
email: marc.chaumont@lirmm.fr

Camera Model Identification With The Use of Deep Convolutional Neural Networks

Keywords: Camera Identification, Deep Learning, Convolutional Neural Network, Fully Connected Network

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I. INTRODUCTION

Source camera identification is the process of determining which camera device has been used to capture an image. It is used in security and legal issue as an evidence [START_REF] Kirchner | Forensic camera model identification[END_REF]. As a relation to prior work, researchers have proposed to use the artifacts that exist in the camera pipeline to collect specific features manually and use them to distinguish between camera models or individual devices.

We can classify the camera identification approaches in two families. The first family groups the methods that require to compute a model (PRNU, radial distortion) to identify a camera and then evaluate a statistical proximity (correlation) between the model and the image to test. Lukas et al. [START_REF] Lukas | Digital camera identification from sensor pattern noise[END_REF] propose a source camera device identification using the sensor pattern noise as a fingerprint for uniquely identifying sensors. Choi et al. [START_REF] Choi | Source camera identification using footprints from lens aberration[END_REF] use the lens radial distortion. Since each camera model expresses a unique radial distortion pattern, it is used as a fingerprint to help on its identification. Dirik et al. [START_REF] Dirik | Source camera identification based on sensor dust characteristics[END_REF] use the sensor dust patterns in digital single lens reflex cameras (DSLR) as a method for device identification.

The second family regroups the methods based on machine learning and feature vector extraction. Here, the model is built by the classification algorithm knowing the features. In order to identify a camera, the classifier evaluate the proximity (distance) between a previously learned model, and the feature vector of the image to test. Bayram et al. [START_REF] Bayram | Improvements on source camera model identification based on cfa interpolation[END_REF] determine the correlation structures presented in each color band in relation with the CFA interpolation. Kharrazi et al. [START_REF] Kharrazi | Blind source camera identification[END_REF] extract 34 features (color features, Image Quality Metrics (IQM), and wavelet domain statistics) and used them to perform camera model identification. Celiktutan et al. [START_REF] Celiktutan | Blind identification of source cell-phone model[END_REF] use a subset of Kharrazi's feature sets and the features of binary similarity measures to identify the source cell-phone camera. Filler et al. [START_REF] Filler | Using sensor pattern noise for camera model identification[END_REF] introduce a method of camera model identification from features of the statistical moments and correlations of the linear PRNU pattern. Gloe et al. [START_REF] Gloe | Feature-based forensic camera model identification[END_REF] used Kharrazi's feature sets with extended color features to identify camera models. Xu and Shi [START_REF] Xu | Camera model identification using local binary patterns[END_REF] also proposed the camera identification using machine learning through Local Binary Patterns as features. Wahab et al. [START_REF] Abdulwahab | Inter camera model image source identification with conditional probability features[END_REF] used the conditional probability as a single feature set to classify camera models. Marra et al. [START_REF] Marra | Evaluation of residual-based local features for camera model identification[END_REF] proposed 338 SPAM features from the Rich Models [START_REF] Fridrich | Rich models for steganalysis of digital images[END_REF] based on co-occurrences matrices of image residuals. Tuama et al. [START_REF] Tuama | Source camera identification using features from contaminated sensor noise[END_REF] developed a method for digital camera model identification by extracting three sets of features: co-occurrences matrix, traces of color dependencies features related to CFA interpolation arrangement, and conditional probability statistics.

From the state of the art mentioned above, CNN approach has not been used for camera identification. In the field of digital forensics Bayar et al. [START_REF] Bayar | A deep learning approach to universal image manipulation detection using a new convolutional layer[END_REF] proposed a deep learning approach to detect image manipulation, while Chen et al. [START_REF] Chen | Median filtering forensics based on convolutional neural networks[END_REF] introduced the convolutional neural networks in median filtering forensics. The general focus of machine learning is the representation of the input data and the generalization of the learning patterns. Good data representation can lead to high performance. Thus the key point is to construct features and data representations from raw data. Feature design consumes a large portion of the effort in a machine learning task, and is typically domain specific.

Deep Learning algorithms are one of the promising research fields into the automated extraction of complex data representations at high levels of abstraction. A key benefit of deep learning is that the analysis and learning of massive amounts of unsupervised data make it a valuable tool for Big Data Analysis. Thus, deep learning often produces good results [START_REF] Najafabadi | Deep learning applications and challenges in big data analytics[END_REF]. Nevertheless, we must say that deep learning approaches require high computing resources compared to more traditional machine learning approaches. Indeed it necessitates a powerful GPU and a big database.

However, using a CNN as a black box leads to a weak performance in identifying camera model. Thus in this paper, we evaluate the obtained gain to modify the CNN model proposed by Krizhevsky [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. We also experimentally compare our CNN model to AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], and to GoogleNet [START_REF] Szegedy | Going deeper with convolutions[END_REF].

The rest of this paper is organized as follows. Section II explains the concept of CNN and its relation to general machine learning concept. Section III presents all the details of our best CNN architecture for camera model identification. While in Section IV, we describe the experiments and the results. Conclusion comes in Section V.

II. CONVOLUTIONAL NEURAL NETWORKS CNNS

Recently, Deep learning with the use of Convolutional Neural Networks (CNNs) have achieved wide interest in many fields. Deep learning frameworks are able to learn feature representations and perform classification automatically from original images. Convolutional Neural Networks (CNNs) have shown impressive performances in artificial intelligence tasks such as object recognition and natural language processing [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF].

The general structure of a CNN consists of layers composed of neurons. A neuron takes input values, does computations and passes the result to the next layer. The general structure of a CNN is illustrated in Figure 1 which also shows the similarities with traditional machine learning approach. The next subsections describe the CNNs layers.

A. Convolutional layers & Classification layers

A conventional layer consists of three operations: convolution, the activation function, and pooling. The result of a convolutional layer is called a feature map, and can be considered as a particular feature representation of the input image. The convolution can be formulated as follows:

a l j = n i=1 a l-1 i * w l-1 ij + b l j , (1)
where * denotes convolution, a l j is the j-th output map in layer l, w l-1 ij is convolutional kernel connecting the i-th output map in layer l -1 and the j-th output map in layer l, b l j is the training bias parameter for the j-th output map in layer l, and n is the number of feature maps from layer l -1.

The activation function is applied to each value of the filtered image. There are several types of the activation function such as, an absolute function f (x) = |x|, a sine function f (x) = sinus(x), or Rectified Linear Units (ReLU) function f (x) = max(0, x).

The next important step is the pooling. A pooling layer is commonly inserted between two successive convolutional layers. Its function is to reduce the spatial size of the representation and to reduce the amount of parameters and computation in the network. During the pooling, a maximum or an average is computed.

The last process done by a convolutional layer is the normalization of the feature maps. The normalization is applied on the feature maps in order to obtain comparable output values for each neuron.

The classification layer consists of fully connected layers and a softmax function. In a fully connected layer, neurons have full connections to all activations in the previous layer. The activations can be computed with a matrix multiplication followed by a bias offset. The fully connected layer will compute the class scores by the softmax function. In this way, CNNs transform the original image from pixel values to the final class scores [START_REF] Najafabadi | Deep learning applications and challenges in big data analytics[END_REF].

B. Learning process

When the learned features pass through the fully connected layers, they will be fed to the top layer of the CNNs, where a softmax activation function is used for classification. The back propagation algorithm is used to train the CNN. The weights and the bias can be modified in the convolutional and fully connected layers due to the error propagation process. In this way, the classification result can be fed back to guide the feature extraction automatically and the learning mechanism can be established.

The CNN architecture has millions of parameters which may arise overfitting problem. Drop out technique is used for reducing overfitting. It consists of setting the output of each hidden neuron with probability 0.5 to zero. The neurons which are dropped out in this way do not contribute to the forward pass and do not participate in backpropagation. This technique increases robustness, since a neuron can not rely on the presence of particular other neurons. It is, therefore, forced to learn more robust features that are useful in conjunction with many different random subsets of the other neurons [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF].

III. THE PROPOSED CNN DESIGN FOR CAMERA MODEL

IDENTIFICATION The framework of our proposed model is shown in Figure 2, where we describe the detailed settings of the architecture. The first layer is the filter layer, followed by three convolutional layers from the first (Conv1) to the third (Conv3). While the last three layers are the fully-connected layers (FC1, FC2, FC3) for the classification. The details of our CNN model is illustrated in the following subsections.

A. Filter layer

The classical way for denoising an image is to apply a denoising filter. For each image I, the residual noise is extracted by subtracting the denoised version of the image from the image itself as follows:

N = I -F (I), (2)
where F (I) is the denoised image, and F is a denoising filter. This filter will be used in our experiments and applied on each color channel separately.

Another denoising high-pass filter is used on the input image I. This filter is the one used by Qian et al [START_REF] Qian | Deep learning for steganalysis via convolutional neural networks[END_REF]. Applying this type of filter is important in the proposed method since it can suppress the interference caused by image edges and textures in order to obtain the image residual as follows:

A = I * 1 12       -1 2 -2 2 -1 2 -6 8 -6 2 -2 8 -12 8 -2 2 -6 8 -6 2 -1 2 -2 2 -1       (3)
The output of this step will fed the CNN. In our experiments, we examined two types of filters as a preprocessing. The first one is the high pass filter adopted by Qian et al [START_REF] Qian | Deep learning for steganalysis via convolutional neural networks[END_REF] and the second one is the well known wavelet based denoising filter [START_REF] Fridrich | Digital image forensic using sensor noise[END_REF].

B. Convolutions

AlexNet Convolutional Neural Networks [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] is adapted and modified to fit the model requirements. The first convolutional layer (Conv1) treats the residual image with 64 kernels of size 3 × 3. The size the feature maps produced is 126 × 126. Then the second convolutional layer (Conv2) takes the output of the first layer as the input. It applies convolutions with kernels of size 3×3 and produces feature maps of size 64×64. The third convolution layer applies convolutions with 32 kernels of size 3 × 3. The Rectified Linear Units (ReLUs) is a non-linearity activation function which is applied to the output of every convolutional layer. ReLUs is considered as the standard way to model a neurons output and it can lead to fast convergence with large models trained on large datasets [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF].

The third convolutional layer is followed by a max pooling operation with window size 3×3 , which operate on the feature map in the corresponding convolutional layer, and lead to the same number of feature map with decreasing spatial resolution.

C. Fully Connected layers

The fully-connected layers (FC1) and (FC2) have 256, and 4096 neurons respectively. ReLUs activation function is applied to the output of fully connected layer. Each of (FC1) and (FC2) are dropped out during the learning. The output of last fully connected layer (FC3) is fed to a softmax function.

IV. EXPERIMENTS AND EVALUATION

For the evaluation of the experiments, we used 33 camera models from two different data sets. The first set is made of 27 camera models from Dresden database [START_REF] Gloe | The 'Dresden Image Database' for benchmarking digital image forensics[END_REF], and the second set is 6 personal camera models. The list is given in Table I with the notice that all the images of the same model came from the same device. Using such different data sets ensure the diversity in the used data base. Before any further manipulation, The data set is subdivided into training and testing sets, such that 80% of the data set is chosen for the training and the rest 20% for the testing data.

In order to fit the CNN model conditions, we sub-divided the chosen data set images into 256×256 and we ignored those of less than 256 × 256. By applying the images sub-division step, we obtain a bigger data set which is beneficial for the training process. When doing the training/testing subdivision into two sets, we make sure that different parts of the same I shows all camera models with their number of images. For each experiment, the data set is chosen randomly and the results are averaged after running the procedure 5 times with 5 different splitting of the database. The experiments are done with a single GPU card of type GeForce GTX Titan X manufactured by Nvidia, and DIGITS training system. Many experiments were done to achieve the design of the CNN model. We measure the efficiency of the CNNs by looking at the minimum error rate after convergence. Our CNN model is shown in Figure 2 and detailed in Section III. By applying two different filters, explained in subsection III-A, we have two different residuals which are referred to as Residual1 (high-pass filter), and Residual2 (wavelet noise filter) in the three different experiments.

Experiment 1

The first experiment uses the first 12 camera models given in Table I. For each image in the data set, a residual1 is extracted by applying a high pass filter [START_REF] Qian | Deep learning for steganalysis via convolutional neural networks[END_REF]. Our CNN model is trained on the resulted residuals of the 12 camera models. Then we use it to identify the source camera model of each image in the test set to construct the identification accuracy.

The confusion matrix of the classification results are shown in Table III. The average accuracy achieved by this experiment is 98%. From Table III, we can see that the best identification accuracy is recorded for the camera model Kodak -M 1063 which achieves 99.89%. Agf a-Sensor -530s, Canon-55, F ujif ilm -F ineP ix -J50, P anasonic -DM C -F Z50, and Samsung-L74wide also achieved semi-perfect accuracy rates. While P raktica -DCZ5.9 recorded the least accuracy rate which is 90.44%. Before going on in the experiments, it is important to evaluate the influence of the pooling layer. By adding a pooling layer for two convolutional layers, we achieve 94.23%, whereas without pooling, it was 93.88% . This result increased to 98.09% for three convolutional layers with max-pooling layer. The results of adding a pooling layer to the model is resumed in Table II.

The experiments reference as Residual2 is obtained by applying a wavelet denoising filter [START_REF] Lukas | Digital camera identification from sensor pattern noise[END_REF] on each image in the data set, then subtract the denoised image from the original one. Residuals of the training set fed the CNN model to perform the training process. This part achieves 95.1% as total identification accuracy for the 12 camera models which is 3% lower compared to Residual1. The confusion matrix of this part are shown in Table IV. With Residual2, the best identification accuracy is recorded for the camera model P anasonic -DM C -F Z50 which achieves 99.46%. While P raktica -DCZ5.9 recorded the least accuracy rate which it is 81.54%. We can hypothesize that the residuals obtained from such a filter suppress too much features related to some characteristic of the acquisition pipeline of a given camera model like the CFA interpolation, or lens-aberration correction traces, and that is exactly what the CNN model need to learn about the camera model features.

Experiment 2

The experiment is re-performed on the first 14 camera models of Table I, by adding SonyDSC -H50 and SonyDSC -W 170 to the data set of experiment 1. This experiment achieved 97.09%, and 93.23% as a total identification accuracy for residual1, and residual2 respectively. The total identification accuracy is shown in Table V. The identification accuracy decreased with these two models due to the fact that the captured images from camera models of the same manufacturer are sometimes harder to separate, such as SonyDSC -H50 and SonyDSC -W 170. This is due, as it has been observed in [START_REF] Kirchner | Forensic camera model identification[END_REF], to the strong feature similarity of some camera models from the same manufacturer.

Experiment 3

The proposed CNN model is performed again with all the 33 camera models given in Table I. We achieve 91.9% as an identification accuracy for the 33 camera models for Residual1. As we can see, the accuracy is decreased as the number of models is increased, and this is a known behavior in machine learning approach, especially when increasing the number of classes [START_REF] Gloe | Feature-based forensic camera model identification[END_REF]. For Residual2, the experiments are less useful since the results are lower compared to Residual1. The results for the three data sets of camera models [START_REF] Marra | Evaluation of residual-based local features for camera model identification[END_REF][START_REF] Tuama | Source camera identification using features from contaminated sensor noise[END_REF]33) are shown in Table V.

Comparison with AlexNet and GoogleNet

AlexNet was developed by Alex Krizhevsky et al. [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], and GoogleNet was designed by Szegedy et al. [START_REF] Szegedy | Going deeper with convolutions[END_REF]. These two CNNs models are trained on our data sets to be compared with our proposed CNN model. The results are illustrated in Table V. GoogleNet consists of 27 layers which explain the higher score it achieves. For experiment 1, with 12 camera models, AlexNet achieves 94.5%, and 91.8% for for Residual1, Residual2 respectively. GoogleNet achieves 98.99%, and 95.9% for Residual1, Residual2 respectively. We achieved with 12 camera models, 98% and 95.1% for Residual1, Residual2 respectively.

The trend is similar for the experiments with 14 camera models. AlexNet achieves 90.5% (respectively 89.45%) for Residual1 (respectively Residual2). We achieve 97.09% (respectively 93.23%) for Residual1 (respectively Residual2) and GoogleNet achieves 98.01% (respectively 96.41%) for Residual1 (respectively Residual2).

We see that our proposition improves AlexNet with 7% for the 14 camera models and the efficiency is only 1% above the bigger network of GoogleNet. As a complexity measure, the time expended for training 12 camera models using our proposed CNN model is about 5 hours and a half, while the time expended for training the same set using GoogleNet is about 16 hours. The time expended by our model for testing 12 camera is about 10 minutes against 30 minutes for GoogleNet. We conclude that our CNN model has good performance for a really smaller complexity compared to GoogleNet.

We should also add that compared to the state of the art approaches based on classical feature extraction and machine learning, the obtained results are similar with a proposition such as [START_REF] Tuama | Source camera identification using features from contaminated sensor noise[END_REF]. The two methods are implemented in different conditions since the classical machine learning approach [START_REF] Tuama | Source camera identification using features from contaminated sensor noise[END_REF] uses the full resolution of the data set while the proposed CNN method uses images of size 256×256. GoogleNet gives similar global accuracy (98.99%) with the same set of 14 models. This is thus a good point for CNNs approaches.

By achieving the perfect design of CNNs and well tuning the network we can achieve more than the classical methods listed in the state of the art. V. CONCLUSION

In this paper, we evaluate the efficiency of using CNNs for source camera model identification based on deep learning and convolutional neural networks. The contribution represents a big challenge since it is quite different from existing conventional techniques for camera identification. We tried a small net by tuning the AlexNet model. This small network is nevertheless slightly less efficient (1% to 3%) than the biggest GoogleNet model. The varying results with the two different preprocessing filters show the important role that the preprocessing plays in the overall classification accuracy.

Scalability has also been evaluated and the increase of the number of models decreases the accuracy not too drastically. Increasing the number of layers seems to be promising and future work should explore bigger networks such as ResNet of Microsoft [START_REF] Kaiming | Deep residual learning for image recognition[END_REF] (which consists of more than 150 layers).

Fig. 1 .

 1 Fig. 1. The Conventional Neural Networks Concept.

Fig. 2 .

 2 Fig. 2. The layout of our Conventional Neural Networks for Camera Model Identification.

TABLE I CAMERA

 I MODELS USED IN THE EXPERIMENTS, MODELS MARKED WITH * ARE OF PERSONAL CAMERA MODELS WHILE ALL THE OTHERS ARE FROM DRESDEN DATABASE.

				Original	No. images
	Seq.	Brand	Model	Resolution 256 × 256
	1	Agfa Photo	DC-733s	3072x2304	30349
	2	Agfa Photo	DC-830i	3264x2448	39204
	3	Agfa Photo	Sensor 530s	4032x3024	55585
	4	Canon	Ixus 55	2592x1944	15680
	5	Fujifilm	FinePix J50	3264x2448	22680
	6	Kodak	M1063	3664x2748	64960
	7	Nikon	D200 Lens A/B	3872x2592	55800
	8	Olympus	M1050SW	3648x2736	28560
	9	Panasonic	DMC-FZ50	3648x2736	37100
	10	Praktica	DCZ 5.9	2560x1920	14630
	11	Samsung	L74wide	3072x2304	24948
	12	Samsung	NV15	3648x2736	30380
	13	Sony	DSC-H50	3456x2592	36920
	14	Sony	DSC-W170	3648x2736	28700
	15 Agfa Photo	DC-504	4032x3024	10074
	16 Agfa Photo	Sensor505-x	2592x1944	12040
	*17	Canon	EOS-1200D	3648x2736	26780
	*18	Canon	PowerShot SD790 IS 3648x2736	30016
	19	Canon	Ixus70	3072x2304	20196
	20	Canon	PowerShotA640	3648x2736	26320
	*21	Canon	EOS7D	3648x2736	9360
	22	Casio	EX-Z150	3264x2448	19548
	23	Nikon	CoolPixS710	4352x3264	37944
	24	Nikon	D70	3008x2000	13860
	25	Nikon	D70s	3008x2000	13706
	*26	Nikon	D5200	3648x2736	34500
	27	Pentax	OptioA40	4000x3000	27885
	28	Pentax	OptioW60	3648x2736	26880
	29	Ricoh	GX100	3648x2736	26880
	30	Rolli	RCP-7325XS	3072x2304	21384
	*31	Sony	DSC-HX50	3648x2736	15960
	*32	Sony	DSCHX60V	3648x2736	44400
	33	Sony	T77	3648x2736	25340
	original image do not belong, in the same time, to the training
	and testing sets. Table

TABLE II RESULTS

 II FOR THE FIRST 12 CAMERA MODELS CONSIDERING THE POOLING PROCESS FOR Residual1.

	Proposed Method	Accuracy
	Two convolutional layer without Pooling	93.88%
	Two convolutional layer with max Pooling	94.23%
	Three convolutional layer with max Pooling	98.0%

TABLE V IDENTIFICATION

 V ACCURACIES FOR ALL THE EXPERIMENTS COMPARED TO ALEXNET AND GOOGLENET.

		Exp 1	Exp 2	Exp 3
		(1-12) models	(1-14) models	(1-33) models
	Method	residual 1 residual 2 residual 1 residual 2 residual 1
	AlexNet	94.50%	91.8%	90.50% 89.45%	83.5%
	GoogleNet 98.99%	95.9%	98.01% 96.41%	94.5%
	Proposed Net 98.00%	95.1%	97.09% 93.23%	91.9%

ACKNOWLEDGMENT

This work was partially funded and supported by the Ministry of Higher Education and Scientific Research in Iraq, Northern Technical University.