Adversarial Robustness Of Deep Learning Models
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Adversarial Robustness of Deep Learning Models
Efficient operation and control of modern day urban systems such as transportation networks is now more important than ever due to huge societal benefits. Low cost network-wide sensors generate large amounts of data which needs to processed to extract useful information necessary for operational maintenance and to perform real-time control. Modern Machine Learning (ML) systems, particularly Deep Neural Networks (DNNs), provide a scalable solution to the problem of information retrieval from sensor data. Therefore, Deep Learning systems are increasingly playing an important role in day-to-day operations of our urban systems and hence cannot not be treated as standalone systems anymore. This naturally raises questions from a security viewpoint. Are modern ML systems robust to adversarial attacks for deployment in critical real-world applications? If not, then how can we make progress in securing these systems against such attacks? In this thesis we first demonstrate the vulnerability of modern ML systems on a real world scenario relevant to transportation networks by successfully attacking a commercial ML platform using a traffic-camera image. We review different methods of defense and various challenges associated in training an adversarially robust classifier. In terms of contributions, we propose and investigate a new method of defense to build adversarially robust classifiers using Error-Correcting Codes (ECCs). The idea of using Error-Correcting Codes for multi-class classification has been investigated in the past but only under nominal settings. We build upon this idea in the context of adversarial robustness of Deep Neural Networks. Following the guidelines of code-book design from literature, we formulate a discrete optimization problem to generate codebooks in a systematic manner. This optimization problem maximizes minimum hamming distance between codewords of the codebook while maintaining high column separation. Using the optimal solution of the discrete optimization problem as our codebook, we then build a (robust) multi-class classifier from that codebook. To estimate the adversarial accuracy of ECC based classifiers resulting from different codebooks, we provide methods to generate gradient based white-box attacks. We discuss estimation of class probability estimates (or scores) which are in itself useful for real-world applications along with their use in generating black-box and white-box attacks. We also discuss differentiable decoding methods, which can also be used to generate white-box attacks. We are able to outperform standard all-pairs codebook, providing evidence to the fact that compact codebooks generated using our discrete optimization approach can indeed provide high performance. Most importantly, we show that ECC based classifiers can be partially robust even without any adversarial training. We also show that this robustness is simply not a manifestation of the large network capacity of the overall classifier. Our approach can be seen as the first step towards designing classifiers which are robust by design. These contributions suggest that ECCs based approach can be useful to improve the robustness of modern ML systems and thus making urban systems more resilient to adversarial attacks.
Evaluating and Understanding Adversarial Robustness in Deep Learning
Deep Neural Networks (DNNs) have made many breakthroughs in different areas of artificial intelligence. However, recent studies show that DNNs are vulnerable to adversarial examples. A tiny perturbation on an image that is almost invisible to human eyes could mislead a well-trained image classifier towards misclassification. This raises serious security concerns and trustworthy issues towards the robustness of Deep Neural Networks in solving real world challenges. Researchers have been working on this problem for a while and it has further led to a vigorous arms race between heuristic defenses that propose ways to defend against existing attacks and newly-devised attacks that are able to penetrate such defenses. While the arm race continues, it becomes more and more crucial to accurately evaluate model robustness effectively and efficiently under different threat models and identify those ``falsely'' robust models that may give us a false sense of robustness. On the other hand, despite the fast development of various kinds of heuristic defenses, their practical robustness is still far from satisfactory, and there are actually little algorithmic improvements in terms of defenses during recent years. This suggests that there still lacks further understandings toward the fundamentals of adversarial robustness in deep learning, which might prevent us from designing more powerful defenses. \\The overarching goal of this research is to enable accurate evaluations of model robustness under different practical settings as well as to establish a deeper understanding towards other factors in the machine learning training pipeline that might affect model robustness. Specifically, we develop efficient and effective Frank-Wolfe attack algorithms under white-box and black-box settings and a hard-label adversarial attack, RayS, which is capable of detecting ``falsely'' robust models. In terms of understanding adversarial robustness, we propose to theoretically study the relationship between model robustness and data distributions, the relationship between model robustness and model architectures, as well as the relationship between model robustness and loss smoothness. The techniques proposed in this dissertation form a line of researches that deepens our understandings towards adversarial robustness and could further guide us in designing better and faster robust training methods.
Adversarial Robustness for Machine Learning
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. - Summarizes the whole field of adversarial robustness for Machine learning models - Provides a clearly explained, self-contained reference - Introduces formulations, algorithms and intuitions - Includes applications based on adversarial robustness