Bizhan Alipour Pijani
email: bizhan.alipourpijani@loria.fr

Abdessamad Imine
email: abdessamad.imine@loria.fr

Michaël Rusinowitch

Online Attacks on Picture Owner Privacy

Keywords: Social Network, Attribute Privacy, Online Inference Attack

We present an online attribute inference attack by leveraging Facebook picture metadata (i) alt-text generated by Facebook to describe picture contents, and (ii) comments containing words and emojis posted by other Facebook users. Specifically, we study the correlation of the picture's owner with Facebook generated alt-text and comments used by commenters when reacting to the image. We concentrate on gender attribute that is highly relevant for targeted advertising or privacy breaking. We explore how to launch an online gender inference attack on any Facebook user by handling online newly discovered vocabulary using the retrofitting process to enrich a core vocabulary built during offline training. Our experiments show that even when the user hides most public data (e.g., friend list, attribute, page, group), an attacker can detect user gender with AUC (area under the ROC curve) from 87% to 92%, depending on the picture metadata availability. Moreover, we can detect with high accuracy sequences of words leading to gender disclosure, and accordingly, enable users to derive countermeasures and configure their privacy settings safely.

Introduction

Facebook is the dominant platform for users to interact, share pictures, and stay connected to their friends. A Facebook user has a profile, a list of friends, and a digital record of behaviors. For instance, a user's behavioral data comprise the list of pages liked by the user. Attribute inference attacks are emerging threats to user privacy in social media such as Facebook. Previous attribute inference attacks focus on inferring a user's private attributes (e.g., location, gender, sexual orientation, and/or political view) by leveraging either social friendship structures [START_REF] Zhenqiang | You are Who you Know and How you Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors[END_REF] or user behaviors [START_REF] Abdelberi | You are What you Like! Information Leakage Through Users' Interests[END_REF]. Attribute inference attacks can be significantly improved by natural language processing techniques (NLP ) since most Facebook data is textual or can be represented as a text. The recent development of deep learning for NLP allows one to capture semantic relations between words from their vectorial representations, lead to an efficient attribute inference attack [START_REF] Perozzi | Deepwalk: Online Learning of Social Representations[END_REF]. The approach also applies to non-textual communication modes by using Emoji2vec [START_REF] Eisner | emoji2vec: Learning Emoji Representations from their Description[END_REF].

The problem of inferring user's attributes becomes qualitatively different if social structures and user behaviors are not available, which is often the case in the real scenario. Unlike previous studies, we show how to detect Facebook user's gender online through his/her shared images along with Facebook generated alttext and received comments underneath those pictures. In online attacks: (i) the attacker relies on offline analysis knowledge to predict new target user attributes, and (ii) input data are collected online by an attacker crawling the targeted profile. The attacker constructs the offline analysis knowledge by collecting profiles with known attributes (in our case gender) and employs sophisticated techniques (e.g., NLP ) to capture patterns and structures from collected data. Moreover, Facebook users can build their offline analysis knowledge and consider themselves as online targeted users to check their vulnerability to attribute inference attacks.

Machine learning classifiers, and/or vector representation techniques accurately infer private attributes from users' public data (e.g., page likes). Inferred attributes can be employed to deliver personalized advertisements [START_REF] Cadwalladr | How cambridge analytica turned facebook 'likes' into a lucrative political tool[END_REF] or privacy breaking [START_REF] Belinic | Personality Profile of Social Media Users How to Get Maximum from It[END_REF]. In [START_REF] Farahbakhsh | Analysis of Publicly Disclosed Information in Facebook Profiles[END_REF], the authors investigate Facebook users' privacy awareness and show one-half of the 479k examined Facebook users hide their gender. Facebook users prefer to hide their gender for two reasons. First, they want camouflage against sexual harassment and stalking. The Facebook search bar lets users track down pictures of their female friends, but not the male ones [START_REF] Lenton | Facebook Wants you to Search Photos of your Female Friends at the Beach, but not your Male Mates[END_REF]. Second, they want to reduce discrimination. The American Civil Liberties Union (ACLU ) 1 accused Facebook of enabling employers to use targeting technology that excludes women from receiving job ads for some positions.

While many Facebook users hide their sensitive attributes (e.g., gender, age, political view), pictures are still available to public. A social media sharing analysis conducted by The New York Times revealed that 68% of their respondents share images to give people a better sense of who they are and what they care about. Users in social media share pictures to receive feedback for their activities, especially from friends, and acquaintances, provide a great sense of connectedness. However, they lose privacy control on their posted pictures due to extra information (i.e., meta-data) added by third-party during the publication process. For any uploaded photos, Facebook implemented an object detection system to provide automatically a set of tags, called alt-text, that describe pictures content. They propose this technique to generate a description that can be used by a text reader for blind users. An attacker can use these tags to relax image processing tasks. Furthermore, when observing a picture on Facebook, people write instinctive comments to express their feeling. Automatically generated alt-text and comments (picture metadata) contain potentially sensitive information available to an attacker.

Problem Statement. We propose a method to infer online the target user gender by using non-user generated data. This method even applies to Facebook users who are cautious about their privacy and hide any type of available in-formation (e.g., friend list, liked pages, groups, and attributes) on their profile. Since our training dataset only contains 25,456 unique words, the input data of an online attack may contain words that do not occur in this training dataset. The new words are called out of vocabulary words (OOV ). To circumvent this problem, we rely on the pre-trained vectors of an advanced NLP model, namely word2vec [START_REF] Mikolov | Efficient Estimation of Word Representations in Vector Space[END_REF], and its version dedicated to emojis emoji2vec [START_REF] Eisner | emoji2vec: Learning Emoji Representations from their Description[END_REF]. Word2vec and emoji2vec, abbreviated by WE2V, are trained on large datasets (e.g., Wikipedia for word2vec) with specific writing structure or usage. Therefore their pre-trained vectors should be adapted when we aim to apply them to a specific domain such as Facebook. Retrofitting technique [START_REF] Faruqui | Retrofitting Word Vectors to Semantic Lexicons[END_REF] is called for adjusting the WE2V pretrained vectors by combining external knowledge (WE2V dataset), and internal knowledge (offline collected words/emojis co-occurrence). A simple and direct approach to handle an out of vocabulary word would be to replace it by a synonym. However, this approach fails for our gender inference problem, as the word and its synonym can orient to different genders. An example taken from our dataset illustrates this point: male-posted pictures receive more comments containing the word gorgeous, while a synonym of this word, namely beautiful, is used more frequently for commenting female posted pictures. To that end, we use cosine similarity score [START_REF] Mikolov | Efficient Estimation of Word Representations in Vector Space[END_REF] to compute similarities among words/emojis, including OOV words. Our approach assumes the following hypotheses:

1. The commenter's gender is hidden. As a consequence, standard homophilybased methods do not apply. 2. The target user is careful enough to conceal gender information in his/her posted comments. Therefore it is needless to process the comments written by the target user. 3. The user profile name does not disclose gender information as Facebook users often use pseudos.

Contributions. Our contributions and improvements over previous works are:

1. A new online attribute inference attack based on non-user generated data composed of alt-text created by Facebook to describe the content of the picture, and comments containing words and emojis posted by friends, friends of friends, or regular users. 2. A strategy for adapting pre-trained vectors for Facebook by exploiting offline scraped comments. 3. A privacy-enhancing system that pinpoints received comments or posted pictures leading to a gender inference attack.

Outline. The paper is organized as follows: we review related work in Section 2.

In Section 3, we overview the system architecture. Section 4 defines the gender inference attack. Section 5 presents in detail the offline attack steps. Section 6 presents the online attack steps. Section 7 shows experimental results. In Section 8, we discuss the attack process, and we conclude in Section 9.

Related Work

Profiling has gained great attention in the past decade. Deriving user gender, for instance, is important for recommendation systems. Recently, researchers have investigated social media platforms in order to distinguish males and females from content sharing [START_REF] De Choudhury | Gender and Cross-cultural Differences in Social Media Disclosures of Mental Illness[END_REF] and behavior [START_REF] Singh | Inferring Gender of a Twitter User Using Celebrities it Follows[END_REF]. Prior works claim that gender prediction is possible from the writing style [START_REF] Flekova | Analyzing Biases in Human Perception of User Age and Gender from Text[END_REF], word usage [START_REF] Sap | Developing Age and Gender Predictive Lexica over Social Media[END_REF] of the target user. Gender inference from the target user name can be performed across major social networks [START_REF] Karimi | Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods[END_REF]. However, the performance of this type of attack is biased towards countries of origin [START_REF] Santamaría | Comparison and Benchmark of Nameto-gender Inference Services[END_REF]. The authors in [START_REF] Cheung | An Analytic System for User Gender Identification Through User Shared Images[END_REF] propose user gender identification through user shared images in Fotolog and Flickr, two image-oriented social networks. They perform image processing on each crawled image (in offline mode), which is not feasible with online attacks. The diversity and global usage of emojis lead researchers to analyse emoji usage according to gender. The authors in [START_REF] Chen | Through a Gender Lens: Learning Usage Patterns of Emojis from Large-scale Android Users[END_REF] collect the data with Kika Keyboard and investigate user preferences. This work has two drawbacks that degrade the performance: (i) opposite-gender friends interaction may affect user emoji usage [START_REF] Nguyen | Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment[END_REF], and (ii) user cautiousness in choosing the emojis. Our work is different in two senses. First, we skip the target user emoji usage and rather rely on other Facebook users' emotional responses to solve the above limitations. Second, we exploit the idea that the picture content has a powerful impact on individuals' emotional responses.

To sum up, in contrast with previous works, we study gender inference attacks on Facebook by considering words, and emojis preferences of other Facebook users (e.g., friends) when commenting pictures published by the target user. We do not explore the user network, which has two advantages: (i) makes the attack feasible even when target personal data and his/her ego-network is unavailable, and (ii) makes the attack suitable for online mode. We showed the benefit of non-user generated data analysis to infer the picture owner's gender [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF][START_REF] Alipour | You are What Emojis Say About your Pictures: Language-independent Gender Inference Attack on Facebook[END_REF]. This work is different from our previous works. First, our attack is not limited to textual language, or emojis as we combine words, and emojis. Second, we propose an online gender inference attack. Third, we leverage sophisticated words, and emojis vector representation, word2vec, and emoji2vec, to handle out of vocabulary words, and emojis.

Figures 1 and2 depict the overall architecture of our system. First, we overview the offline training components, and next, we present our online attack ingredients.

Offline Training. This procedure combines domain specific and external knowledge in the following way (see Figure 1). Data Crawling collects Facebook users' data in an offline mode for training gender classifiers. Then Data Preprocessing prunes, cleanses and normalizes the collected data. Feature Extraction and Feature Selection derive a set of features that contribute the most to gender inference from an initial set obtained by n-grams and correlation of alt-text and comments. Retrofitting is the process of adjusting an existing word/emoji vector representation using extra knowledge (in our case, offline collected words/emojis co-occurrence). It allows us to fit WE2V word vector representations to our specific domain, namely Facebook. Machine Learning aims to select the best gender classifier among the one that we have trained, using standard evaluation metrics. We discuss in detail all the steps in Section 5. Pre-processing and N-grams prunes raw data and extracts features compose of words/emojis or sequence of words/emojis. Word/Emoji Similarity finds similarity between word/emoji vectors in the vector space representation. Gender Classification applies to the target user based on extracted features. Comment Filtering proposes to hide received comments or published pictures that considered to be sensitive to gender. Finally, after hiding the suggested comments, Gender Obfuscation re-runs the gender inference attack steps to check whether the gender information secretes. These steps are detailed in Section 6.

Attack Description

In this work, we consider an attacker who intends to infer a picture owner gender g by observing a set of pictures P . Each published picture p, where p ∈ P contains metadata (a set of comments c p , and generated alt-text a p ). The attacker can be anyone who can crawl data from Facebook. To accurately infer the target user's gender, the attacker has to cover three possible scenarios. In the first scenario, pictures receive no comments, or comments are unavailable due to privacy reasons (the target user conceals all comments underneath the picture). The attacker can still learn female and male preferences in picture sharing style, and infer target gender from tags or alt-texts. In the second scenario, Facebook is unable to generate automatic alt-texts due to the quality of the posted picture. The attacker learns words/emojis usage from comments posted by other Facebook users to infer the target gender. In the third scenario, both comments and alt-text are publicly available. The attacker can leverage both Facebook users' words/emojis usage and generated alt-text (tags) to infer the target gender. The gender inference attack is based on computing Mutual Information (MI ) on all users labeled by gender g given Facebook generated alt-text a, and/or other Facebook users posted comments c for each picture p. Let X be a random variable that takes value 1 when the posted photo contains a in the first scenario (resp., c in the second scenario, resp., both a and c in the third scenario) and 0 when the published picture does not contain a in the first scenario (resp., c in the second scenario, resp., both a and c in the third scenario). Let Y be a random variable that takes value 1 (resp., 0) if the picture owner is female (resp., male). Then, we compute MI as follows:

M I(X; Y )ac = x∈{0,1} y∈{0,1} P (X = x, Y = y)aclog2 P (X = x, Y = y)ac P (X = x)acP (Y = y)ac (1)
where P (X = x) ac and P (Y = y) ac are the marginal probabilities of x and y, and P (X = x, Y = y) ac is their joint probability. Based on M I, the most likely target gender for a set of pictures P is:

arg max g∈{0,1} M I(X; Y ) ac (2)
which is the core concept behind our inference attack. Let x i u be the number of occurrence of feature i in user pictures metadata (a or c). MI measures the mutual dependence between picture owner gender and generated alt-text (resp., received comments, resp., alt-text and received comments) in the first scenario, (resp., second scenario, resp., third scenario). Each user u with set of pictures P , and gender g can be represented by a feature set x u = {x 1 u , x 2 u , .., x n u }, and the label (or class) y u , where y u ∈ {0, 1}. In the offline mode, the attacker trains machine learning algorithms with samples (x u , y u ), for all u ∈ U training as inputs, where x u = {x 1 u , x 2 u , .., x n u } and U training is a set of users. In the online mode, the attacker carries out the attack on a chosen target user u new , by using the features obtained from trained algorithms. We discuss feature selection and extraction techniques in Subsection 5.3.

Offline Training

In this section, we introduce and discuss the offline components that we implement to train our machine learning algorithms.

Data Crawling from User Context

Let U be the set of Facebook users, where u i is the ith user with a set of posted pictures P i = {p 1 i , p 2 i , .., p n i }. For user u i , we extract g i , A i , C i where g i is the user gender, A i is a set of alt-text presented by Facebook and C i is the set of comments posted by other Facebook users for P i .

Data Pre-processing

Difficulty in analyzing data from social media raises from the presence of different kinds of textual errors, such as misspellings and grammatical errors. In addition to pre-processing steps detailed in [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF][START_REF] Alipour | You are What Emojis Say About your Pictures: Language-independent Gender Inference Attack on Facebook[END_REF], we perform spelling correction to correct word's spelling and/or intentionally repeated characters (as in soooooooo beautiful ). However, not all misspelled words can be handled by NLP spell correction techniques. For example, love u contains a deformation letter u that refers to pronoun you that can be considered as a misspelled letter a, or luv is an abbreviation form of love you. We re-formulate misspelled words that cannot be corrected by spell correction techniques.

Features Selection and Extraction

Feature selection is the process of identifying and selecting relevant features correlated to variables of interest (gender, in our case). The purpose of feature selection is three-fold: promoting the model prediction performance, providing faster and efficient classifiers, and reducing the data dimensionality to decrease the model complexity. We select features in two different ways:

1. Contiguous Sequence of Word/Emoji. We compute n-grams to capture the occurrences of words/emojis in comments, and tags in alt-text in a given window size (n) for each gender. Our experiments revealed that 4-grams, and 5-grams are best suited for comments, and alt-text, respectively [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. By retaining only n-grams that appear more than 50 times in total, we collect 2797 features. Table 1(a), and (b) show the discriminative alt-texts generated for female and male-owned pictures, and Facebook users' words/emojis preferences while commenting them, respectively. Additionally, we compute the probability of a person being male or female, given the picture generated alt-text, or words/emojis. For example, p(f emale| ) is the probability that the user is female if she receives comments with emoji from other Facebook users.

2. Correlation of Alt-text and Comments. We construct a co-occurrence matrix to find the correlation between gender, alt-text, and received comments to distinguish females from males. The matrix records co-occurrences of words, emojis, and tags in the same picture, not necessarily in a given window size n.

We drop rare co-occurrence pairs that appear less than 50 times in total. In that way, we collect 2103 features from all the possible combinations of words/emojis, and alt-text in our data set. Table 1(c), and (d) take into account the correlation of generated alt-text with received words and emojis, respectively. We also compute the probability for a person to be male or female, given picture alt-text, and received words/emojis. In total, we select 4900 features from the above categories. After choosing these features, we apply feature extraction algorithms to downsample the features and retain only the ones that contribute the most to gender prediction. We evaluated individual and combined feature extraction methods to derive the best features set [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. We apply these methods to find the best feature set W best .

Retrofitting Words/Emojis Vectors

After selecting the best feature set, we compute vector representations of these features to evaluate the similarity of the online collected words from the target profile (which may contain new words or sequences of words) to our best feature set. To that end we use word2vec and emoji2vec. Our goal is to create a set of embeddings that accounts for both our offline collected dataset, OCD, and original word/emoji representations learned from WE2V. Retrofitting [START_REF] Faruqui | Retrofitting Word Vectors to Semantic Lexicons[END_REF] is a process that adjusts an original word vector separately using a knowledge graph (e.g. WordNet [START_REF] Miller | WordNet: a Lexical Database for English[END_REF]), in our case OCD instead. Retrofitting has advantages of being (i) a post-processing operation that does not require to browse the corpus again, (ii) applicable to any vector model, and (iii) simple and fast to implement. Retrofitting computes a new vector v i for the word/emoji w i ∈ W best , with the objective of being close to w i 's original vector v i , when it exists, and also to vectors v j representing w j that are the w i 's nearest overlapped words/emojis in WE2V or OCD. For that, we try to minimize this objective function:

n i=1 αi vi -v i 2 + j:w j ∈W E2V γij vi -vj 2 + j:w j ∈OCD βij vi -vj 2 (3)
We set α i = 1 when w i ∈ WE2V and 0 otherwise. An overlapped word/emoji belongs by definition to both OCD and WE2V datasets. The distance between a pair of vectors is defined to be the Euclidean distance. For w i in WE2V \ OCD we take β ij = 0 and γ ij is the Cosine Similarity score between v i and nearest overlapped words/emojis vectors v j in WE2V dataset. Cosine similarity is widely used to measure the similarity between two non-zero vectors by measuring the cosine of angle between them. For w i in OCD, we take γ ij = 0 and β ij is the Pointwise Mutual Information (PMI ) score [START_REF] Church | Word Association Norms, Mutual Information, and Lexicography[END_REF] between w i , and overlapped co-occurring words w j . PMI has been extensively used in the field of NLP to measure words closeness based on their co-occurrence probability. PMI is formulated as:

P M I(wi, wj ) = log p(wi, wj ) p(wi)p(wj )
where p(w i ), and p(w j ) represent the probabilities that a comment contains w i , or w j , and p(w i , w j ) represents the probability that a comment contains both w i and w j ). Therefore, we calculate the vector v i by taking the average of the nearest overlapped words/emojis vectors v j , considering their cosine similarity γ ij , or pointwise mutual information β ij score according to the cases as follows:

vi = j:w j ∈W E2V γij vj + j:w j ∈OCD βij vj + αiv i j:w j ∈W E2V γij + j:w j ∈OCD βij + αi (4)
The advantage of adjusting the pre-trained words/emojis vector by using offline extracted data co-occurrences is two-fold: (i) handling non-overlapped words/emojis easily, and (ii) using sophisticated distributional embeddings (WE2V ) that make the retrofitted vectors robust and suitable for gender inference attack process. For the feasibility of the computation, we truncate each sum in Equation 4 by summing only the 10 most significative terms (corresponding to the closest words to w i ). In the case of having a sequence of words as the best feature, we first retrofit each word vector separately. Then we take the average of the vectors associated with the words in the sequence. [START_REF] Pagliardini | Unsupervised Learning of Sentence Embeddings Using Compositional N-gram Features[END_REF]. For example, consider beautiful lady as a best feature, we first retrofit beautiful and lady separately. Next, we get a vector for beautiful lady by averaging the retrofitted vectors of beautiful and lady. Figure 3 illustrates the separate word retrofitting, where the blue dots are word2vec vectors, and orange dots are the retrofitted vectors.

Online attack and gender obfuscation

Using our offline knowledge, the online phase consists of classifying a target user with unknown gender to the male or female category. For the demonstration purpose, we assume the target user is given.

Pre-processing and N-grams

This step follows by reformulating the words to their normal form, as detailed in Subsection 5.2. Next, as W best contains both single and sequences of words, n-grams (up to 3-grams) permit to find new words or sequences of words in the online extracted comments.

Word/Emoji Similarity

We count the online occurrences of words belonging to W best and set the value of w best feature, where w best ∈ W best to its number of occurrence. Otherwise, we find the closest words to w best by using their vector representation and set the w best value to the number of occurrence of close words. For each new word w new , we compute the closest w best as follows:

arg max wnew,w best cosine(w new , w best )

We ignore the result if the cosine similarity value is less than 50%. For sequence of words in w new we proceed as in Subsection 5.4. This mechanism of replacing a new word by its closest word in the best feature set allows one to handle out of vocabulary words.

Gender Classification

In this step, the attacker has to evaluate the trained machine learning algorithm in an online mode. Given a target user, the algorithm outputs (i) female, (ii) male, or (iii)unknown. The output depends on the prediction probability threshold. We set this threshold to be 0.70. For example, the output is female if the algorithm gives prediction probability of 0.70 to female, and 0.30 to male. Moreover, the output is unknown if the algorithm prediction probability for female is 0.65, and 0.55 for male. In our experiments (see Section 7), we present the result of 700 users as they are labelled female, or male. Although the threshold empirically derived from our dataset, it is an arbitrary choice to be adapted to other datasets. It helps to prevent inaccurate attacks due to a lack of input information. For example, if the user has only one picture with an alt-text.

Comment/Picture Filtering

We now describe the filtering option proposed by our system for protecting user privacy. First, we discuss comment filtering, and later we define the picture filtering option.

Comments Filtering Option. It suggests users hide comments that contain sensitive words, or sequence of words that disclose picture owner gender. Facebook offers two comments filtering options. First, the users can set up a list of words, phrases, or emojis that they do not want to receive from commenters. Facebook hides matching comments entirely from the published photos. Second, the users can manually select the comments and make them invisible from photos. The advantage of hiding comment is that it is still visible to the commenter and his/her friends, which reduces tension between the commenter and the picture owner.

Pictures Filtering Option. As a suggestion, the user can hide the picture if the generated alt-text or combination of alt-text and comments leak the picture owner gender. Facebook settings allow users to restrict the picture visibility. We use LIME [START_REF] Tulio Ribeiro | Why Should I Trust you?" Explaining the Predictions of any Classifier[END_REF] to alert the user about comments, or pictures that reveal owner gender and require to be hidden. LIME computes an explanation as a list of features (with weights) that either contribute to the prediction (in green) or are evidence against it (in red). Figure 4(a) presents the most contributing features, while, Figure 4(b) shows the least contributing features for a user, labelled by female.

As for gender obfuscation, the user can select some most contributing features as Figure 4(a), according to his/her desires, and then hides the comments, or pictures containing those features by following the above steps. Next, the user can re-run our online system component to check the gender inference attack vulnerability. The user can repeat the process until the output is unknown.

Experiments

In this section, we evaluate our approach for all three scenarios and demonstrate offline and online experiments.

Offline Experiments. Using a Python crawler, we have randomly collected 627,776 pictures and their 1,332,219 comments. Facebook was unable to generate alt-text for 24833 pictures. We have kept those pictures for our second attack scenario, where we rely only on words/emojis usage for commenting pictures. The experiments are achieved by applying the classifiers from Python library scikit-learn. For result robustness, we apply several supervised machine learning algorithms such as Logistic Regression, Random Forests, K-Nearest Neighbors, Naive Bayes and Decision Tree. To evaluate the classifier, we select the same number of males and females to prevent bias classification. Train-test splitting was preferable in this study as it runs k-times faster than k-fold. We choose the train-test size to be 70-30, which gives the best accuracy. To address the problem of fairly estimating the performance of each classifier, we set aside a validation dataset. We train and adjust the hyper-parameters to optimize the performance of classifiers by using this dataset. Eventually, we evaluate the classifiers on the test dataset. Considering the extracted gender as the groundtruth, to evaluate our attack, we compute the AUC-ROC curve. The AUC-ROC curve is a performance measurement for classification problems at various threshold settings. In Figure 5, we show the AUC-ROC results for all three scenarios. In the first scenario, we rely on alt-text to infer the picture owner's gender. Figure 5(a) displays trained algorithms results on the extracted alt-texts features. Based on that, our trained algorithms can infer the target user gender with an AUC of 87%. In the second scenario, we conduct an inference attack by training algorithms only on commenters' comments. As illustrated in Figure 5(b), the performance increases to 90% AUC. In the third scenario, we train the classifiers by using the co-occurrence of alt-texts and words/emojis. Based on the Figure 5(c), Logistic Regression model which had 87% AUC in the first scenario, and 90% in the second scenario gets 5%, and 2% AUC boost in this scenario, respectively, which is a fairly substantial gain in performance.

To conclude, Logistic Regression performs the best in all scenarios. It is a discriminative model that is appropriate when the dependent variable is binary (i.e., has two classes). The results confirm our hypothesis that gender and picture contents have an impact on Facebook users' emotional responses. As a result, an attacker can train standard classifiers by using pictures metadata contained (i) other Facebook users' words/emojis preferences, and (ii) generated alt-text to infer the picture owner's gender. Note, as we rely solely on non-target generated data, the results cannot be compared to previous works that exploit data published by the target.

Online Experiments. We have applied our online experiment to 700 users with their 21,713 pictures and their 64,940 corresponding comments. We have evaluated the performance of each classifier with AUC-ROC. As illustrated in Figure 5 (d,e, andf), Logistic Regression outperforms other classifiers in all three scenarios. Notably, the combination of alt-text and words/emojis boosts the performance of the classifiers in comparison to the other scenarios. To sum up, Logistic Regression is a suitable classifier for this task that can be trained by an attacker to perform a gender inference attack in online mode.

Discussion

The best scenario for the attacker is, as expected, the third scenario when he has access to Facebook generated alt-text and commenters' comments. The attack may work even when the target publishes nothing else than pictures. By applying our system to his published pictures, a Facebook user can check if he/she is vulnerable to gender inference attack of the above type. To counter the aforementioned privacy violations, we offer two countermeasures, namely: (i) hide some comments, or (ii) hide some pictures when they strongly contribute to the attack as explained in Subsection 6.4.

Conclusion

Identifying users' gender from their online activities and data sharing behavior is an important topic in the growing research field of social networks. It provides an opportunity for targeted advertising, profile customization, or privacy attacks. This study has investigated 627,776 pictures and their 1,332,219 comments. Based on the intensive analysis of the shared images, this work has demonstrated (i) a new perspective of gender inference attack on Facebook users by relying on non-user generated data, and (ii) a privacy protection system. We have shown the possibility of gender inference attack even when all user attributes/activities such as profile attributes, friend list, liked pages, and joined groups are hidden.

As future work, we plan to use sophisticated algorithms to take the output of LIME and give the user the exact comments or pictures to be hidden in order to be obfuscated. We also plan to extend the current work to deal with the online inference of other attributes (e.g., age) and to explore online inference in other social network platforms (e.g. Twitter, Instagram). We may also take advantage of combining several user-generated content from different online social networks to infer private attributes.

Fig. 1 :Fig. 2 :

 12 Fig. 1: Offline training

Fig. 3 :

 3 Fig. 3: Retrofitting : (a) Beautiful, (b) Lady

Fig. 4 :

 4 Fig. 4: Lime output : (a) Most contributed features (b) Least contributed features

Fig. 5 :

 5 Fig. 5: AUC result of logistic regression trained on: (a) only alt-text (b) only commenters reactions (c) both alt-text and commenters reactions (d) removing alt-text features (e) removing commenters reactions features (f) removing alttext and commenters reactions features.

Table 1 :

 1

			alt text	MI p(female-alt text ) p(male-alt text)
			closeup smiling people smiling 0.012 0.020 0.012	0.79 0.73 0.71	0.21 0.27 0.29			0.043 0.025 0.014	0.84 0.82 0.87	0.16 0.18 0.13
		(a)	1 person smiling closeup 0.007 0.008	0.68 0.87	0.32 0.13		(b)	0.007 0.007	0.79 0.83	0.21 0.17
			person smiling 0.006	0.77	0.23			beautiful 0.006	0.90	0.10
			person closeup 0.005	0.84	0.16			0.003	0.36	0.64
			beard car selfie	0.004 0.004 0.003	0.23 0.25 0.81	0.77 0.75 0.19			bro gorgeous 0.002 0.003 0.002	0.05 0.85 0.32	0.95 0.15 0.68
		alt text+word 1 person, beautiful 0.042 MI p(female-alt text +word) p(male-alt text+word) 0.94 0.06 closeup, beautiful 0.030 0.92 0.08		alt text+emoji MI p(female-alt text+emoji) p(male-alt text+emoji) 1 person, 0.037 0.80 0.20 1 person, 0.032 0.81 0.17
		1 person gorgeous 0.018	0.39	0.61		closeup,	0.022	0.88	0.12
	(c)	smiling, beautiful 0.017 1 person, pretty 0.016	0.87 0.89	0.12 0.11	(d)	closeup, 1 person,	0.018 0.015	0.83 0.89	0.17 0.11
		closeup, gorgeous 0.011	0.42	0.58		1 person,	0.013	0.80	0.20
		closeup, pretty 0.011	0.90	0.10		1 person,	0.010	0.90	0.10
		smiling, pretty 0.007 selfie, beautiful 0.006 1 person, cute 0.005	0.89 0.85 0.80	0.11 0.15 0.20		smiling, beard, beard,	0.009 0.005 0.001	0.87 0.13 0.30	0.13 0.87 0.70

word/emoji MI p(female-word/emoji) p(male-word/emoji) MI result: (a) alt-text, (b) words/emojis, (c) correlation of alt-text and words, (d) correlation of alt-text and emoji.

https://www.aclu.org/blog/womens-rights/womens-rights-workplace/ facebook-settles-civil-rights-cases-making-sweeping

This work is supported by DIGITRUST (http://lue.univ-lorraine.fr/fr/ article/digitrust/).


Rémi Dumasdelage

Metropole Nice Côte D'azur

Nice

Olivier Delestre

Simulating coliform transport and decay from 3D hydrodynamics model and in situ observation in Nice area

In coastal area, raw wastewater can be directly discharged in the sea during extreme rain events or failure of the sewage network. The highly urbanized and touristic area of Nice is directly exposed to this risk. In order to protect the bathing population and to apply the European regulation (DCE, 2000), Nice municipality has decided to work on a forecasting tool of transport and fate of fecal bacterial indicators such as Escherichia Coli.

This paper deals with the first step of this approach which consists in developing a three-dimensional hydrodynamic simulation coupled with water quality parameters. The model used is based on Reynolds-Averaged Navier-Stokes's equations for the calculation of the velocities and water depth (TELEMAC3D), and wave energy balance equation to account for the wave induced current (TOMAWAC). Two field measurements were conducted on a real wastewater pollution coming from a sewage pipe located at -38m depth.

The first campaign has validated the vertical diffusion of the effluent through the water column by the means of variable depth samples and has stressed the hourly variability of sewage bacterial quality. Due to the lack of data regarding this last point, the model could not be fully validated. The second campaign has been designed to overcome this issue and to gather all the data needed to proceed to the validation of our hydro-ecological simulation. The comparison of the field measurements and the simulations has successfully demonstrated the accuracy of the model. But the uncertainty associated with the laboratory analysis (MPN) are high, making the calibration and validation processes a complex task.

Nevertheless, further research needs to be done regarding the variability of microbial quality in order to develop an operational tool allowing the forecast of bacterial pollution. The next step of this study will be to establish the hourly variation of microbial quality in the wastewater during dry and raining periods.

Introduction

Nice is a highly urbanized town of the French Riviera (population: 500 000) with an economy mainly based on tourism (fig. 1). Famous for its gravel beach and its sea front called Promenade des Anglais, this area hosts around 4.3 millions of tourists during the peak season in summer with a number of people on the beach estimated to 20 000 persons per day during July and August.

Nice coast is composed of 4.5 km of pebble cordon divided by a coastal river mouth called le Paillon located on figure 1. Under the West part of the beach and the East part of the sea front the main pipe of the town sewer system is buried 10 feet underground and transports sewage to a treatment plant.

The majority of Nice sewer system is handling both sanitary waste and storm water. During heavy rainfalls the sewer can be overloaded and the overflowing untreated water is discharged through seven marine outfalls. The sewage discharged in the sea can lead to bacterial pollution events. In this geographical area, such events were never monitored and simulated before the experiment presented in this paper.

The directive 2006/7/CE was enacted by the European Union to reduce the number of these events and so improve the bathing water quality [START_REF]Directive 2006-7-EC of the European Parliament and of the Council of 15 February 2006 concerning the management of the bathing water quality and repealing Directive 76-160-EEC[END_REF]. This legislative text lays down provisions for the monitoring, the 1 Figure 1: Location of Nice and its 7 marine outfalls classification, the communication and the management of bathing water quality. The directive was transposed to the French law in September 2008 by the Decree N2008-990 [START_REF]relatif à la gestion de la qualité des eaux de baignade et des piscines[END_REF]. Two main points were developed for coastal waters:

• the first concerns the parameters monitored which are the microbiological indicators: Escherichia coli and intestinal enterococci are quantified by concentration values in colony forming unit per 100 ml (cfu/100ml);

• the second point is the bathing water which has to be classified as 'poor', 'sufficient', 'good', or 'excellent' based on the microbiological concentration values (see tab. 1 and 2). If bathing water is classified as poor for five consecutive years, a permanent bathing prohibition or permanent advice against bathing shall be introduced.

Intestinal Interococci

Excellent Table 2: Concentration thresholds for Escherichia Coli in cfu/100ml and the 95% confidence interval from the most probable number of bacteria [START_REF] Mcgrady | Tables for Rapid Interpolation of Fermentation-tube Results[END_REF], [START_REF] Halvorson | Application of statistics to problems in bacteriology, Chap. 1: A means of determining bacterial population by dilution method[END_REF] In France, the municipalities are legally responsible for protecting public health. As a consequence, the bathing water management falls directly within their jurisdiction [START_REF]Article 2212-2 sur le pouvoir de police du Maire et la définition du bon ordre, la sûreté, la sécurité et la salubrité publiques[END_REF]. The sewer system of Nice and its geographical situation make it sensible to the microbiological pollution events. During the last four years the city has faced 10 legal thresholds overun in the summer season. Thus, to preserve public health, to optimize the water bath quality, and to stick with the directive requirements, Nice municipality has decided to develop its knowledge regarding the microbiological pollution.

The main objective of this paper is to present the first step of this project which consists in developing a three-dimensional hydrodynamic simulation of microbiological water quality parameters. Firstly, the models used are introduced (i) as well as the field experiments carried out to validate them (ii). Then this paper reviews the results of these measurements (iii) and the accuracy of the 3D-simulations developed is discussed (iv).

Method

Reynolds-Averaged Navier-Stokes code coupled with a wave energy balance equations solver

The temperature and the salinity of the sea water can change rapidly through the water column [START_REF] Bedri | A Three-dimensional Hydro-environmental Model of Dublin Bay Zeinab Bedri[END_REF]. In the Nice bay, these physical changes have been deeply studied by measurement campaigns [START_REF] Dolle | Suivi écologique du milieu marin, surveillance du rejet urbain, études des flux a la mer et qualité de l'eau pour la communauté d'agglomération de Nice Cote d'Azur[END_REF]. The variability of the temperature and the salinity are linked with meteorological parameters (wind, the exterior temperature) and hydrodynamic driving forces [START_REF] Muhammetoglu | Prediction of wastewater dilution and indicator bacteria concentrations for marine outfall systems[END_REF]. Thus, the use of 3D modelling tools was compulsary to represent the impact of this variabilty on the convection and diffusion transport of microbiological pollution [START_REF] Gourmelon | Microbial modelling in coastal environments and early warning systems: useful tools to limit shellfish microbial contamination[END_REF]. TELEMAC-3D solves the Reynolds-Averaged Navier-Stokes equations [START_REF] Hervouet | Hydrodynamics of free surface flows modeling with the finite element method[END_REF], [START_REF] Pedlosky | Geophysical fluid dynamics[END_REF], [START_REF] Bedri | A Three-dimensional Hydro-environmental Model of Dublin Bay Zeinab Bedri[END_REF] with the incompress-ible form of the mass conservation equation ( 1) and the non-conservative momentum equations (2-4):

∂U ∂x + ∂V ∂y + ∂W ∂z = 0 (1) ∂U ∂t + U . ∇(U ) = - 1 ρ 0 ∂p ∂x + ∂ ∂x (ν H ∂U ∂x ) + ∂ ∂y (ν H ∂U ∂y ) + ∂ ∂z (ν V ∂U ∂z ) + F x (2) ∂V ∂t + U . ∇(V ) = - 1 ρ 0 ∂p ∂y + ∂ ∂x (ν H ∂V ∂x ) + ∂ ∂y (ν H ∂V ∂y ) + ∂ ∂z (ν V ∂V ∂z ) + F y (3) ∂W ∂t + U . ∇(W ) = -g - 1 ρ 0 ∂p ∂z + ∂ ∂x (ν H ∂W ∂x ) + ∂ ∂y (ν H ∂W ∂y ) + ∂ ∂z (ν V ∂W ∂z ) + F z (4)
where U , V and W are the components of the velocity vector U in the x, y and z directions respectively; p is the pressure at the depth z; ρ 0 is the reference density value; ν H and ν V are the horizontal and vertical eddy viscosity respectively; g is the acceleration of gravity and F x, F y, F z are the external forces components such as Coriolis force, centrifugal force or the wind forcing, in the x, y and z directions respectively. The non-hydrostatic pressure assumption is used in this study. Therefore, the acceleration and eddy viscosity terms for the vertical velocity component of the momentum equation are taken into account through the dynamic pressure p d [START_REF] Pedlosky | Geophysical fluid dynamics[END_REF]. Hence, the total pressure p can be expressed as the sum of this dynamic pressure p d which includes the vertical acceleration and the vertical viscosity term, and the hydrostatic term p h which represents the atmospheric pressure on the surface and the weight of the water column above it:

p = p atm + ρ 0 g(Z s -z) + ρ 0 g Zs z ∆ρ ρ 0 dz p h +p d , (5)
where Z s is the free surface elevation; p atm is the atmospheric pressure; ρ is the density of water;

The studied area is 60 km 2 wide and 200 m deep, so the vertical length cannot be neglected compared to the horizontal scales [START_REF] Chen | A comparison of hydrostatic and nonhydrostatic pressure components in seiche oscillations[END_REF]. This observation justifies the use of the non-hydrostatic assumption expressed previously.

The density varies with the sea water temperature T and the salinity S. Even if these variations are considered as minor in the incompressible form of the mass conservation equation, these two parameters will be taken into account using the simplified equation of state established by UNESCO [START_REF][END_REF], [START_REF] Hervouet | Hydrodynamics of free surface flows modeling with the finite element method[END_REF]:

ρ = ρ ref (1 -(T (T -T ref ) 2 -750S)10 -6 ), (6)
where T ref is the reference temperature of 277.15K and ρ ref is the reference density at the temperature T ref when the salinity is zero.

The temperature and salinity are interacting with the hydrodynamics driving forces. Thus they are considered as active tracers C r and will be simulated through a non-conservative equation based on the mass conservation equation [START_REF] Hervouet | Hydrodynamics of free surface flows modeling with the finite element method[END_REF]:

∂C r ∂t + ( U . ∇)C r = ∇.(ν Cr ∇(C r )) + Q, ( 7
)
where Q is the source or sink term; ν Tr is the diffusion coefficient of the tracer. This equation is also used to model the mean concentration of Escherichia Coli C ec (cfu/100ml).

∂C ec ∂t + ( U . ∇)C ec = ∇.(ν Cec ∇(C ec )) + Q ec . (8)
The decay of E.Coli is defined in the OPENTELEMAC by the sink term Q ec expressed as [START_REF] Bedri | A Three-dimensional Hydro-environmental Model of Dublin Bay Zeinab Bedri[END_REF]:

Q ec = - 2.303 T 90 .C ec , (9)
where T 90 is the time expressed in second during which the bacterial population reduces by 90%. Contrarily, the temperature and the salinity, E.Coli is a passive tracer. It has no interaction with the hydrodynamic of the flow. The inactivation process of the fecal bacteria in the sea depends on environmental factors which are mainly the light intensity [START_REF] Feitosa | Coupling bacterial decay and hydrodynamic models for sewage outfall simulation[END_REF], the temperature and the salinity [START_REF] Lopez | A method for the source apportionment in bathing waters through the modelling of wastewater discharges: Development of an indicator and application to an urban beach in Santander (Northern Spain)[END_REF], [START_REF] Muhammetoglu | Prediction of wastewater dilution and indicator bacteria concentrations for marine outfall systems[END_REF]. Amongst the formulations established in this field [START_REF] Bellair | Significance of diurnal variations in fecal coliform die-off rates in the designof ocean outfalls[END_REF], [START_REF] Chamberlin | A decay model for enteric bacteria in natural waters[END_REF], [START_REF] Mancini | Numerical estimates of coliform mortality rates under various conditions[END_REF], [START_REF] Guillaud | T90 as a tool for engineers: interest and limits[END_REF], [START_REF] Yang | Natural disinfection of wastewater in marine outfall fields[END_REF], the formula proposed by Canteras et al. [START_REF] Canteras | Modelling the coliforms inactivation in the Cantabrian Sea (Bay of Biscay) from in situ and laboratory determinations of T90[END_REF] was used to calculate the inactivation time:

T 90 = 2.303 1.040 T -20 1.012 S 2.533 + 0.113I( c H ) ( 10
)
where c is the light extinction coefficient in the water, H the water depth, and I the light intensity The experiment presented in this study was carried out during the night, so the light intensity terms has been neglected. The decay rate was calculated as 25 hours and supposed to be constant because of the little variability of the water column salinity and temperature presented in the next section (fig. 6). In further applications of this model, a fortran subroutine has been implemented to change the inactivation time at each time step based on these environmental parameters.

The system of equations (1-4) is solved with the Finite Element Method applied to a spatial discretization using prisms. Each prism has six nodes and a vertical quadrangular side [START_REF] Hervouet | Hydrodynamics of free surface flows modeling with the finite element method[END_REF]. The grid domain for Nice has been constructed with the Blue Kenue mesh generator to create a 2D horizontal mesh [START_REF] Nrc | Blue Kenue TM: Software tool for hydraulic modellers[END_REF]. Two zones of different mesh size were constructed on this triangular unstructured grid. The first zone is defined by a resolution of 3m and is centered on the sewage outlet. It includes all the observation points. The second zone is made of 8m triangals and covers the remaining part of the study domain (fig. 2). This 2D mesh has been repeated over the vertical 30 times in order to build the three-dimensional mesh composed of 3187440 elements and 556791 nodes. The vertical density of the mesh is higher around the 40 first meters depth where the wastewater are discharged (Fig. 3 and5).

The three-dimensional hydrodynamic model is coupled with the wave propagation code TOMAWAC (TELEMAC-based Operational Model Addressing Wave Action Computation) [START_REF] Benoit | Development of a third generation shallow-water wave model with unstructured spatial meshing[END_REF]. This phase averaged model calculates the sea states by solving the balance equation of the action density directional spectrum at each point of the horizontal computational grid described previously [START_REF] Goda | Random Seas and Design of Maritime Structures[END_REF], [START_REF] Komen | Dynamics and Modelling of ocean waves[END_REF]. The wave driving forces computed in TOMAWAC are transmitted to TELEMAC3D which calculates the nearshore current [START_REF] Manuel | TOMAWAC Software for sea state modelling on unstructured grids over oceans and coastal seas, Manuel d'utilisateur[END_REF]. This free and opensource integrated suite of solvers have proved its accuracy on several nearshore applications [START_REF] Aelbrecht | 2D and 3D Modelling of wave-driven currents in presence of a groin system in a tidal sea, Coastal Engineering and Marina Developments[END_REF].

In order to set the initial wave and current conditions, a hotstart file containing the modelling sea state observed just before the beginning of the experiment is used. The study domain is initially free of any On the boundary, a parameterized Jonswap spectrum [START_REF] Hasselmann | Measurements of wind-wave growth and swell decay during the joint north sea wave project (jonswap)[END_REF] is set up based on the measurements from Meteo-France wave buoy located 60km from the coast and transferred to the domain limits. The velocities on the outline of the studied area are supposed nil because no current observations was available on a larger scale. To compute heat exchange between water and the atmosphere, a linearised formula expressing the balance of heat exchange fluxes at the free surface is chosen [START_REF] Sweers | Monograms to estimate the heat-exchange coefficient at the air-water interface as a function of wind speed and temperature; a critical survey of some literature[END_REF]

K ∂T ∂z = A ρC p (T -T air ) ( 11
)
where K is the coefficient of moleculare heat diffusion in water, C p is the water specific heat equal to 4140 J/kg/K. A is the exchange coefficient in W/m 2 /K and is expressed according to the temperature of water T and the wind velocity u wind in m/s:

A = (4.48 + 0.049T ) + 2021.5b(1 + u wind )(1.12 + 0.018T + 0.00158T 2 ) ( 12
)
where b is a parameter depending on the location of the study. In the Mediterranean it can be set to 0.0035. Concerning the numerical scheme, the implicit Finite Element Method is solving the diffusion step of tracers and velocities. Regarding the advection of velocities the characteristics method is used and for the advection of tracers the explicit Finite Element Method is applied.

The measurement campaigns methodology

Two measurements campaigns were designed in order to quantify the reliability of the simulation built using the TELEMAC system. Both of them are based on the same pollution events specific to Nice. Each year, in November, the main pipe of the sewer system of Nice is completly cleaned up and checked up by the municipality. These operations are carried out by zones and points of interest. In order to protect the workers, the sewages collected by the section under maintenance are deviated through the marine outfalls. Thus, the pollution event is completly planned and controlled (each outfall is monitored with discharge measurement stations). As a consequence, these events are perfectly adapted to validate our modelling work.

Based on logistical limitations of Nice Municipality, it has been decided to concentrate our efforts on only one outfall: la Païole. Two main criteria have oriented our choice. The first one was the depth of the outfall (30m). At this depth, the effect of the density gradient on the transport of the polluant can be observed. The second criterion is based on the isolation of the outfalls (130m from the coast). In the area of La Païole, there are no other pollution sources, so only one outfall can be monitored.

Four sampling points were defined to follow the spatial distribution of the bacteria concentration: one right at the outlet, one further north near the coast, and two to the east and west (fig. 5).

Regarding the 2015 experiment, at each sampling point, three measurements were taken along the water column using Niskin bottles (fig. 4): at the surface, in the middle and at the bottom of the water column (around -1, -10 and -25 meters depth). These samples were taken at 9:00 PM, midnight, 3:00 AM and 8:00 AM to follow the time evolution of the pollutant concentrations. The 2015 campaign has raised some issues related to this protocol. Firstly, during the campaign, we observed that the outfall was not at the expected position. So we moved the 4 sampling points initialy planned during the night (fig. 5). Secondly the data of flows discharged in the sea were known, but not the bacteria concentration of the raw sewage. And finally the analysis of the samples shows that the pollution propagated only at the surface. This observation will be discussed and explained in the following part.

As a consequence, in November 2016 a new campaign was conducted with an improved protocol. The sampling points were revised based on the new location of the outlet on the map and samples were only collected at the surface. The sampling schedule was also modified for a better capture of the extrema ( 9:00 PM , midnight, 2:00 AM and 6:00 AM). In order to obtain the bacteria concentration of the raw sewage, samples were taken directly from the network upstream of la Païole at 8:00 PM and 3:00 AM.

In 2015 and 2016, the samples were analyzed by the municipality using a method derived from the most probable number method. The latter consists in inoculating numerous dilutions of the sample in enrichment broth tubes. Subsequently, the tubes are transplanted onto selective agar allowing, depending on the number of positive tubes and their distribution, to estimate the most probable number of bacteria (MPN) [START_REF] Mcgrady | Tables for Rapid Interpolation of Fermentation-tube Results[END_REF], [START_REF] Halvorson | Application of statistics to problems in bacteriology, Chap. 1: A means of determining bacterial population by dilution method[END_REF]. Thus, the sample is diluted and then seeded in a series of wells of a microplate which contains the dehydrated culture medium. The microplates are examined under UV after 36 to 72 hours of incubation at the temperature 317.15K. The presence of Escherichia coli and Enterococci is revealed by detection of their enzyme [START_REF] Cofrac | Analyses microbiologiques, biologiques et de biologie moléculaire des eaux[END_REF].

Results

Experimental results

In order to set up the 3D model, the temprature and salinity were followed during the experiment thanks to the Eol Buoy located in Villefranche bay, 6km East of Nice. This sampling tool is able to measure the two physical parameters from the depth of 100m to the surface every hour. The experiments took place in autumn during which the climate is characterised by an air temprature around 293.15K, 10 degrees less than in summer. So the temperature profile is evolving from a stratified state to an homogenous state with values of 291.15K at 50m depth and 291.5K at the surface (fig. 6). Regarding the salinity, the variations observed are not significant. So the salinity profile can be considered as homogeneous on the studied domain (fig. 6). The outlet discharges were carried out using a Détronic automatic captor. The discharge event started at 7:00 PM and finished around 4:30 AM . During 8 hours, 5300 m 3 of raw sewage were released in the sea for both events. During the night the hourly mean is decreasing from 220 l/s to 145 l/s. This evolution illustrates the changes of the individual wastewater-generating activities of this residential dwelling [43]. The variability of the appliance usage is also characterised by instantaneous peak flows reaching 260 l/s. The E.coli concentration results of the 2015 campaign have demonstrated that the bacteriological impact of the raw sewage discharge is spreading through the surface without any contamination of the deeper water (fig. 7). This phenomenon comes from the density of the marine environment and the effluent. In fact, the density of the raw effluent is lower than the salt water described previously because of its temperature and salinity.

Regarding the surface results (Fig. 8), the C1R1 gives the highest bacteriological counts due to its proximity with the outlet. The first concentration measured reaches 70 000 E.Coli/100ml which is 70 times higher than the legal threshold. During the night, the bacteriological count is decreasing until 10 000 E.Coli/100ml at 2:00 AM and 150 E.Coli/100ml at 6:10 AM. The end of the flow, the mortality and the diffusion/advection phenomenon explain the concentration measured during the morning. Nevertheless, the decrease observed between 7:00 PM and 2:00 AM is not correlated with the discharge values which stay high during the event. In 2016, the order of magnitude of the bacteriological counts were equal to the 2015 campaign and the behaviour of the pollutant plume distribution was similar to the previous description (fig. 9).

The raw sewage samplings (C2N1) results demonstrate the variability of the wastewater quality during the night (fig. 10). The important decrease of the feacal contamination are due to the individual wateruses events [START_REF]Management of Small Wastewater Flows[END_REF]. In fact, the water uses pattern changes drasticly from 7:00 PM to 3:00 PM [START_REF] Anderson | The performance of ultra-low-volume flush toilets in Phoenix[END_REF], [START_REF] Anderson | The Impact of Water Conserving Plumbing Fixtures On Residential Water Use Characteristics in Tampa[END_REF], [START_REF] Brown | Residential Water Conservation Projects[END_REF], [START_REF] Mayer | Residential End Uses of Water[END_REF]. These changes in the wastewater quality are at the origin of the decrease of the bacteriological counts observed mainly on the sampling points C1R1 and C2R1.

Modelling results

The 3d model results are compared to the experiments results. The E.Coli concentrations at the outlet of the model were set using the wastewater quality results discussed previously.

Firstly, the vertical diffusion and advection phenomena are well represented in the simulations. In fact, the model calculates the right bacteria concentration on the surface with a vertical propagation limited to 5 meters. This local effect explains why no trace of bacteria were found on the sampling point C1W1, C1W2, C1E1 and C1R1 at 10m and 25m depth (fig. 7).

Moreover, the 2015 E.Coli concentration simulated tends to fit the experiments measurements for the East, West, North points which means that the horizontal diffusion and advection terms are well described (fig. 15).

Regarding the 2016 sampling campaign, the simulation shows poor results on the East point C2E1 (fig. 16). The underestimation of the E.Coli counts could come from the weakness of this local scale to represent the effect of the wind or currents on the transport of the pollutant. But the lack of current and wave data obervations makes a deeper validation of the hydrodynamic driving forces more complicated.

Mesh sensitivity

Three configurations have been tested to study the mesh resolution sensibility. In order to optimize the calculation duration, unstructured grids with variable mesh sizes have been chosen. The first configuration is built with 10m elements in a circle of 120 m around the sewage outlet. Then, the mesh size is increasing gradually to reach 30m at the observation points C2W1 and C2E1. The same setting has been implemented for the second configuration with a minimum grid size around the outlet of 5 meters. And finally the last Figure 16: Escherichia coli concentration simulated and observed at the free surface, 2016 campaign -the vertical error bars represent the 95% confidence interval of the Most Probable Method configuration is the one introduced in section 2.1, the grid size is set to 3m, uniformly from the pipe to the observation points.

Low resolution grid

Medium resolution grid High resolution grid Grid size between 10m and 30m Grid size between 5m and 30m Grid size 3m

Table 3: Description of the grids tested for the sensitivity analysis -in red triangles the 2016 observation points Figures 17,18 and 19 show that the accuracy of the simulation is increasing with the grid resolution. This result can be achieved because the numerical diffusion is reducing gradually with the mesh size. Nevertheless the C2E1 observation concentrations are not reached even with the high resolution grid. Further simulations should have been tested with higher resolution in order to improve these results and to reach the mesh convergence but the calculation capacity of Nice Municipality is limited. In fact, the final goal of this project will be to create an operational tool that will be able to simulate a bacteriological pollution to inform decision maker during pollution events. Thus, the model should give accurate results quicker than the bacteriological laboratory analysis (36h). A number of elements more important than the one in the 3m resolution grid would give a computation time higher than 36hours based on the computational infrastructure of Nice municipality.

Discussion

Two major points were stressed out by the experiment:

• The uncertainty of the counting method for the microbiological indicators is high [START_REF] Man | The probability of Most Probable Numbers[END_REF]. In fact, the samples were treated based on the most probable number of bacteria in the volume of sea water.

For each sample, the confidence limits are illustrated on the graphs by red segments (fig. [START_REF] Dolle | Suivi écologique du milieu marin, surveillance du rejet urbain, études des flux a la mer et qualité de l'eau pour la communauté d'agglomération de Nice Cote d'Azur[END_REF][START_REF]Directive 2006-7-EC of the European Parliament and of the Council of 15 February 2006 concerning the management of the bathing water quality and repealing Directive 76-160-EEC[END_REF]. As observed, the higher the concentration of bacteria is, the more the uncertainity increases. Consequently, the extremly high values observed in the raw effluent samples have to be associated with a large range of possible values explaining the quantiles, the minimum and the maximum illustrated on (fig. 10). This observation impacts directly the calibration and validation process of a bacterial quality model. The input values of concentration that have to be imposed on the source points (outlet, rivers) are mostly unknown or associated with high uncertainties.

• Moreover, the variability of the pollution in the network during the night has been demonstrated in this study. Only two hours (21h, 3h) were investigated. But we can assume that the bacterial count in the network is fluctuating every hour due to the changes in the uses. This hourly variability can explain the differences observed on the model results in which the decrease of the network bacteriological count is supposed to be constant.

In order to accuratly predict the evolution of the bacterial pollution, a deep study of the raw effluent is needed. Several temporal scales must be investigated: hourly and dayly in order to characterise the variation of the E.Coli concentration due to the individual wastewater-generating activities, and seasonaly or based on meteorological events to evaluate the extreme values.

This paper aims at comparing 3D hydrodynamic simulations of coliforms tranport and decay to in situ observations. Compared to other similar studies [START_REF] Gupta | Calibration and validation of far field dilution models for outfall at Worli, Mumbai[END_REF], [START_REF] Huangl | Modeling of CSO-Induced Pollutant Transport in Mt. Hope Bay[END_REF], [START_REF] Liu | Evaluation of marine outfall with three-dimensional hydrodynamic and water quality modeling[END_REF], [START_REF] Thupaki | Budget Analysis of Escherichia coli at a Southern Lake Michigan Beach[END_REF], the choice of these indicators, the frequency of the measurments and the spatial distribution of the monitored points allow to describe the pollution evolution and its transport at a local scale. Even if these microbiological parameters are the main focus of the public decision makers, the performance of the model should also be tested with physical and chemical data such as the salinity, the temperature, the current velocities and the wave heights. Because of the lack of measurement instruments, this work has not been achieved yet.

Conclusion

In a marine environment, the bacteriological pollutions depend on chemical and physical parameters which interact all along water column (salinity, temperature, light intensity, wave actions). As a consequence , it was necessary to set up specific measurement campaigns in order to calibrate and validate a 3D model based on OPENTELEMAC In 2015 and 2016, two campaigns have allowed us to get a better understanding of the bacterial pollution behavior in this field. Their protocol were funded on spatialy distributed measures of microbiological indicators around the outlet and upstream in the network. Added to that, the water column chemical and physical parameters were gathered thanks to a measurement buoy located 50 km away from the pollution source. These experiment results are an ideal data set to develop a three dimensional bacterial quality model because the vertical and horizontal diffusion/convection phenomenon around the source point are clearly identified. They also permit to quantify the variability of the network raw effluents. Even if the data collected must be completed with local physical and chemical observations, the strength of this approach is its reproducibility. Indeed, the same pollution event occurs every year so a more complete campaign can be designed. The Reynolds-Averaged Navier-Stokes equation associated to the non-hydrostatic pressure assumption successfully reproduce the bacterial concentration evolution in the marine environment. Nonetheless, the high confident limits linked to the laboratory analysis of the E.Coli concentrations make the calibration and validation processes complex. Based on these uncertainities, the TELEMAC3D model developped in this paper shows satisfactory results. And more importantly, the tool has sucessfully determined the end of the pollution for both events. This last aspect is a main point in the process of creating a prediction tool for management crisis purpose.

6 Acknowledgement

Figure 2 :

 2 Figure 2: Computational 2D grid of Nice coastal area.

Figure 3 :

 3 Figure 3: Computational 3D grid of Nice coastal area.

Figure 4 :

 4 Figure 4: Sampling using a Niskin bottle.

Figure 5 :

 5 Figure 5: Sampling points location-2015 and 2016 campaign.

Figure 6 :

 6 Figure 6: Water temperature and salinity measured

Figure 7 :

 7 Figure 7: Escherichia coli concentration measured through the water column on the C1R1 sampling point

Figure 8 :

 8 Figure 8: Escherichia coli concentration measured on each sampling points of the 2015 sampling campaign at the free surface -the vertical error bars represent the 95% confidence interval of the Most Probable Method

Figure 9 :

 9 Figure 9: Escherichia coli concentration measured on each sampling points of the 2016 sampling campaign at the free surface -the vertical error bars represent the 95% confidence interval of the Most Probable Method

Figure 10 :

 10 Figure 10: Escherichia coli concentration measured on the network sampling point, 2016 campaign -box plot based on four replicates for each sample

Figure 11 :

 11 Figure 11: 3D field of Escherichia coli concentration around the sewage outlet

Figure 13 :Figure 14 :

 1314 Figure 13: Time dependent 2D fields of Escherichia coli concentration at the free surface

Figure 15 :

 15 Figure 15: Escherichia coli concentration simulated and observed at the free surface, 2015 campaign -the vertical error bars represent the 95% confidence interval of the Most Probable Method

Figure 17 :Figure 18 :

 1718 Figure 17: Escherichia coli concentration simulated and observed at the free surface on the point C2R1-all grids, 2016 campaign

Figure 19 :

 19 Figure 19: Escherichia coli concentration simulated and observed at the free surface on the point C2E1-all grids, 2016 campaign

Table 1 :

 1 Concentration thresholds for Intestinal Interococci in cfu/100ml and the 95% confidence interval from the most probable number of bacteria[START_REF] Mcgrady | Tables for Rapid Interpolation of Fermentation-tube Results[END_REF],[START_REF] Halvorson | Application of statistics to problems in bacteriology, Chap. 1: A means of determining bacterial population by dilution method[END_REF]

	quality Good quality Sufficient

The authors declare that they have no conflict of interest.

We thank Doctor Luisa Passeron-Mangialajo from Laboratory Ecology and Conservation Science for Sustainable Seas of the University of Nice for her assistance with sampling methodology using Niskin bottle. We would also like to show our gratitude to Doctor Grisoni Jean Michel from the Institute of the Sea of Villefranche, CNRS, for sharing the data of the buoy Eol developped by its research team. We thank Julien Larraun from Metropole Nice Côte d'Azur for his help on the simulation work.