Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection

Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. In this paper, we propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF). Most forgery technologies always bring in high-frequency aware cues, which make it easy to distinguish source authenticity but difficult to generalize to unseen artifact types. The masked frequency forgery representation module is designed to explore robust forgery cues by randomly discarding high-frequency information. In addition, we find that the forgery attention map inconsistency through the detection network could affect the generalizability. Thus, the forgery attention consistency is introduced to force detectors to focus on similar attention regions for better generalization ability. Experiment results on several public face forgery datasets (FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.Comment: The source code and models are publicly available at https://github.com/chenboluo/ACM

Web bot detection evasion using generative adversarial networks

Web bots are programs that can be used to browse the web and perform automated actions. These actions can be benign, such as web indexing and website monitoring, or malicious, such as unauthorised content scraping and scalping. To detect bots, web servers consider bots' fingerprint and behaviour, with research showing that techniques that examine the visitor's mouse movements can be very effective. In this work, we showcase that web bots can leverage the latest advances in machine learning to evade detection based on their mouse movements and touchscreen trajectories (for the case of mobile web bots). More specifically, the proposed web bots utilise Generative Adversarial Networks (GANs) to generate images of trajectories similar to those of humans, which can then be used by bots to evade detection. We show that, even if the web server is aware of the attack method, web bots can generate behaviours that can evade detection

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots.

Web bots are programs that can be used to browse the web and perform different types of automated actions, both benign and malicious. Such web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour which reduce their detectability. Several effective behaviour-based web bot detection techniques have been pro- posed in literature. However, the performance of these detection techniques when target- ing malicious web bots that try to evade detection has not been examined in depth. Such evasive web bot behaviour is achieved by different techniques, including simple heuris- tics and statistical distributions, or more advanced machine learning based techniques. Motivated by the above, in this thesis we research novel web bot detection techniques and how effective these are against evasive web bots that try to evade detection using, among others, recent advances in machine learning. To this end, we initially evaluate state-of-the-art web bot detection techniques against web bots of different sophistication levels and show that, while the existing approaches achieve very high performance in general, such approaches are not very effective when faced with only advanced web bots that try to remain undetected. Thus, we propose a novel web bot detection framework that can be used to detect effectively bots of varying levels of sophistication, including advanced web bots. This framework comprises and combines two detection modules: (i) a detection module that extracts several features from web logs and uses them as input to several well-known machine learning algo- rithms, and (ii) a detection module that uses mouse trajectories as input to Convolutional Neural Networks (CNNs). Moreover, we examine the case where advanced web bots utilise themselves the re- cent advances in machine learning to evade detection. Specifically, we propose two novel evasive advanced web bot types: (i) the web bots that use Reinforcement Learning (RL) to update their browsing behaviour based on whether they have been detected or not, and (ii) the web bots that have in their possession several data from human behaviours and use them as input to Generative Adversarial Networks (GANs) to generate images of humanlike mouse trajectories. We show that both approaches increase the evasiveness of the web bots by reducing the performance of the detection framework utilised in each case. We conclude that malicious web bots can exhibit high sophistication levels and com- bine different techniques that increase their evasiveness. Even though web bot detection frameworks can combine different methods to effectively detect such bots, web bots can update their behaviours using, among other, recent advances in machine learning to in- crease their evasiveness. Thus, the detection techniques should be continuously updated to keep up with new techniques introduced by malicious web bots to evade detection