Literatura académica sobre el tema "Deep-Fake detection"

La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stocké sur un passeport électronique ou une base de données) et un gabarit acquis. Cependant, il devient très important de déterminer l'identité d'un individu contre une population entière en un temps très court (quelques secondes). Ceci représente un enjeu important compte tenu de la taille de la base de données biométriques (contenant un ensemble d’individus de l’ordre d’un pays). Par exemple, avant de délivrer un nouveau passeport à un individu qui en fait la demande, il faut faire une recherche d'identification sur la base des données biométriques du pays afin de s'assurer que ce dernier n'en possède pas déjà un autre mais avec les mêmes empreintes digitales (éviter les doublons). Ainsi, la première partie du sujet de cette thèse concerne l’identification des individus en utilisant les empreintes digitales. D’une façon générale, les systèmes biométriques ont pour rôle d’assurer les tâches de vérification (comparaison 1-1) et d’identification (1-N). Notre sujet se concentre sur l’identification avec N étant à l’échelle du million et représentant la population d’un pays par exemple. Dans le cadre de nos travaux, nous avons fait un état de l’art sur les méthodes d’indexation et de classification des bases de données d’empreintes digitales. Nous avons privilégié les représentations binaires des empreintes digitales pour indexation. Tout d’abord, nous avons réalisé une étude bibliographique et rédigé un support sur l’état de l’art des techniques d’indexation pour la classification des empreintes digitales. Ensuite, nous avons explorer les différentes représentations des empreintes digitales, puis réaliser une prise en main et l’évaluation des outils disponibles à l’imprimerie Nationale (IN Groupe) servant à l'extraction des descripteurs représentant une empreinte digitale. En partant de ces outils de l’IN, nous avons implémenté quatre méthodes d’identification sélectionnées dans l’état de l’art. Une étude comparative ainsi que des améliorations ont été proposées sur ces méthodes. Nous avons aussi proposé une nouvelle solution d'indexation d'empreinte digitale pour réaliser la tâche d’identification qui améliore les résultats existant. Les différents résultats sont validés sur des bases de données de tailles moyennes publiques et nous utilisons le logiciel Sfinge pour réaliser le passage à l’échelle et la validation complète des stratégies d’indexation. Un deuxième aspect de cette thèse concerne la sécurité. Une personne peut avoir en effet, la volonté de dissimuler son identité et donc de mettre tout en œuvre pour faire échouer l’identification. Dans cette optique, un individu peut fournir une empreinte de mauvaise qualité (portion de l’empreinte digitale, faible contraste en appuyant peu sur le capteur…) ou fournir une empreinte digitale altérée (empreinte volontairement abîmée, suppression de l’empreinte avec de l’acide, scarification…). Il s'agit donc dans la deuxième partie de cette thèse de détecter les doigts morts et les faux doigts (silicone, impression 3D, empreinte latente) utilisés par des personnes mal intentionnées pour attaquer le système. Nous avons proposé une nouvelle solution de détection d'attaque basée sur l'utilisation de descripteurs statistiques sur l'empreinte digitale. Aussi, nous avons aussi mis en place trois chaînes de détections des faux doigts utilisant les techniques d'apprentissages profonds<br>Biometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection

Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. The truthfulness of online reviews is critical for both consumers and vendors. Genuine reviews can lead to satisfied customers and success for quality businesses, whereas fake reviews can mislead innocent clients, influence customers’ choices owing to false descriptions and inaccurate sales. Therefore, there is a need for efficient fake review detection models and tools that can help distinguish between fraudulent and legitimate reviews to protect the ecosystem of e-commerce sites, consumers, and companies from these misleading fake reviews. Although several fake review detection models have been proposed in the literature, there are still some challenges regarding the performance of these models that need to be addressed. For instance, existing studies are highly dependent on linguistic features, but these are not enough to capture the semantic meaning of the reviews, necessary for improving prediction performance. Furthermore, when analysing a fake review text data stream, concept drift may occur where the input and output relationship of text changes over time, affecting model performance. The concept drift problem and its performance impact on fake review detection models has yet to be addressed. Moreover, existing models have only focused upon the review text, reviewer-based features, or both. They have also used a limited number of behavioural and content features that reduce the accuracy of the detection model. Further, these models have used fine-grained features (e.g., words) or coarse-grained features (e.g., documents, sentences, topics) separately, reducing the detection models’ performance. To address the above discussed research gaps, this research investigates and analyses the performance of different neural network models and advanced pre-trained models for fake review detection. Then, we propose an ensemble model that combines three transformer models to detect fake reviews on semi-real-world datasets. This research also investigates the concept drift problem by detecting the occurrence of concept drift within text data streams of fake reviews, finding a correlation between concept drift (if it exists) and the performance of detection models over time in the real-world data stream. Furthermore, this research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers and provide interpretability of deep learning models. This ensemble model comprises three popular machine learning models: bidirectional long-short-term-memory (Bi-LSTM), convolutional neural network (CNN), and deep neural network (DNN). Additionally, this model provides interpretations to achieve reliable results from deep learning models, which are usually considered as "Black Boxes". For this purpose, the shapley additive explanations (SHAP) method and attention mechanism are used to understand the underlying logic of a model and provide other hints to determine whether it is "unfair". In summary, this research provides the following contributions: (1) Comprehensive survey that analyses the task of fake review detection by providing the existing approaches, existing feature extraction techniques, challenges, and available datasets. (2) This research investigates and analyses the performance of different neural network models and transformers to demonstrate their effect on fake review detection. Experimental results show that the transformer models perform well with a small dataset for fake review detection. Specifically, the robustly optimised BERT pretraining approach (RoBERTa) achieves the highest accuracy. (3) This research proposes an ensemble of three transformer models to discover the hidden patterns in a sequence of fake reviews and detect them precisely. Experimental results on two semi-real datasets show that the proposed model outperforms the state-of-the-art methods. (4) This research provides an in-depth analysis for detecting concept drift within fake review data streams by using two methods: benchmarking concept drift detection methods and contentbased classification methods. The results demonstrate that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models. (5) This research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers. The experimental results on two real-life datasets present excellent performance and outperformed the state-of-the-art methods. Further, the experimental results prove that our proposed model can provide reasonable interpretations that help users understand why certain reviews are classified as fake or genuine. To the best of our knowledge, this research provides the first study that incorporates advanced pre-trained models, investigates the concept drift problem, and the first Interpretable fake review detection approach. The findings of this thesis contribute to both practical and theoretical applications.

Indiana University-Purdue University Indianapolis (IUPUI)<br>The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.

The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.<br>

碩士<br>東吳大學<br>資訊管理學系<br>106<br>The increasing share of the online businesses in market economy has led to a larger influence and importance of the online reviews. Before making a purchase, users are increasingly inclined to browse online forum that are posted to share post-purchase experiences of products and services. However, there are many fake reviews in the real world, consumers can't identify authentic and fake reviews. Fake online shopping reviews are harmful to consumers who might buy misrepresented products. Therefore, we proposed a framework which could detect fake reviews. In this study, we focused on the data on the web forum called Mobile01 and used text mining to deal with textual data including Bag-of-words, Latent Semantic Analysis and word2vec for word representation. Next, we used machine learning to train the model to detect fake review, including SVM, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Finally, we chose three best performance models to vote and hope that these fake reviews samples can be the reference in the future research.