Security Evaluations under Different Threat Models
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Towards Evaluating the Robustness of Neural Networks, Nicholas Carlini, David A. Wagner, 2017IEEE Symposium on Security and Privacy (SP) (Institute of Electrical and Electronics Engineers (IEEE))DOI: 10.1109/SP.2017.49 - Introduces a family of strong optimization-based white-box adversarial attacks (C&W attacks) for thoroughly evaluating model robustness.
Decision-Based Adversarial Attacks, Wieland Brendel, Jonas Rauber, Matthias Bethge, 2018Sixth International Conference on Learning Representations (ICLR 2018)DOI: 10.48550/arXiv.1712.04248 - Presents the Boundary Attack, a highly query-efficient decision-based black-box attack, relevant for evaluating robustness in settings with minimal attacker knowledge.
Adversarial Machine Learning: A Survey, Xiaoyong Yuan, Pan He, Qiming Qu, Rui Xing, Xiaochun Cao, 2019Journal of Parallel and Distributed Computing, Vol. 132 (Elsevier)DOI: 10.1016/j.jpdc.2019.04.011 - Provides a broad overview of adversarial machine learning, including discussions on different threat models, attack types (white-box, black-box, gray-box), and defense strategies.