Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates
Amin Ghiasi, Ali Shafahi, Tom Goldstein
2020年份
57被引次数
9顶会引用
摘要
Defenses against adversarial attacks can be classified into certified and non-certified. Certifiable defenses make networks robust within a certain -bounded radius, so that it is impossible for the adversary to make adversarial examples in the certificate bound. We present an attack that maintains the imperceptibility property of adversarial examples while being outside of the certified radius. Furthermore, the proposed "Shadow Attack" can fool certifiably robust networks by producing an imperceptible adversarial example that gets misclassified and produces a strong ``spoofed'' certificate.
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引用它的顶会 Paper9
- MetaPoison: Practical General-purpose Clean-label Data PoisoningW. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor 等NeurIPS 2020 · 被引用 242 次
- Fixed Neural Network Steganography: Train the images, not the networkVarsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li 等ICLR 2022 · 被引用 59 次
- Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and QuantizationZijie Zhang, Yang Zhou, Xin Zhao, Tianshi Che 等NeurIPS 2022 · 被引用 56 次
- TSS: Transformation-Specific Smoothing for Robustness CertificationLinyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic 等CCS 2021 · 被引用 21 次
- Why adversarial training can hurt robust accuracyJacob Clarysse, Julia Hörrmann, Fanny YangICLR 2023 · 被引用 6 次
它引用的顶会 Paper3
- Certified Robustness to Adversarial Examples with Differential PrivacyMathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu 等S&P 2019 · 被引用 1,022 次
- Towards Stable and Efficient Training of Verifiably Robust Neural NetworksHuan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal 等ICLR 2020 · 被引用 384 次
- Universal Adversarial TrainingAli Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson 等AAAI 2020 · 被引用 210 次
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