Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
University of Münster（明斯特大学）
Moritz Seiler, Heike Trautmann, Pascal Kerschke
备注：
This version has been accepted for publication at the International Joint Conference on Neural Networks (IJCNN) 2020, which is part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually -- been rather difficult. In this work, we introduce a method for generating strong ad-versaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack
School of Computer Science（计算机学院）；University College Dublin（都柏林大学学院）；School of Computer Science and Statistics（计算机与统计学院）；Trinity College Dublin（都柏林圣三一学院）；University of Science（科学大学）
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
Department of Excellence in Robotics and AI, TeCIP, Scuola Superiore Sant'Anna（机器人与人工智能卓越部门，TeCIP，圣安娜高等学院）；Department of Electrical and Electronic Engineering, University of Cagliari（电气与电子工程系，卡利亚里大学）
Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks
Department of Electrical and Computer Engineering, Rutgers University（罗格斯大学电气与计算机工程系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
Sichuan University（四川大学）；Chengdu University of Information Technology（成都信息工程大学）；Shihezi University（石河子大学）；Tianjin University（天津大学）；Huazhong University of Science and Technology（华中科技大学）
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid XiXian New Area Power Supply Company（国家电网西安新区供电公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）；Illinois Institute of Technology（伊利诺伊理工大学）
