This book provides a systematic study on the security of Deep Learning. With its powerful learning ability, Deep Learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that Deep Learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take autonomous driving as an example, there were more than 12 serious autonomous driving accidents in the world in 2018, including Uber, Tesla and other high technological enterprises. Drawing on the reviewed literature, we need to discover vulnerabilities in Deep Learning through attacks, reinforce its defense, and test model performance to ensure its robustness. The book aims to provide a comprehensive introduction to the methods of attacks, defenses, and testing evaluations for deep learning in various scenarios. We focus on multiple application scenarios such as computer vision, Federated Learning, graph neural networks, and Reinforcement Learning, considering multiple security issues that exist under different data modalities, model structures, and tasks. Testing deep neural networks is an effective method to measure the security and robustness of Deep Learning models. Through test evaluation, security vulnerabilities and weaknesses in deep neural networks can be identified. By identifying and fixing these vulnerabilities, the security and robustness of the model can be improved. The book is divided into three main parts: attacks, defenses, and testing. In the attack section, we introduce in detail the attack methods and techniques targeting Deep Learning models.

Attacks, Defenses and Testing for Deep Learning PDF

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