<i>𝜀</i> -weakened robustness of deep neural networks-Reference-Cited by-同舟云学术

𝜀 -weakened robustness of deep neural networks

Published:2022-07-18 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis
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Author:

Huang Pei¹,Yang Yuting²,Liu Minghao¹,Jia Fuqi¹,Ma Feifei¹,Zhang Jian¹

Affiliation:

1. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China

2. University of Chinese Academy of Sciences, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3533767.3534373

Reference48 articles.

1. Anish Athalye , Nicholas Carlini , and David A. Wagner . 2018. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples . In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan , Stockholm, Sweden , July 10-15, 2018 , Jennifer G. Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, 274–283. Anish Athalye, Nicholas Carlini, and David A. Wagner. 2018. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, Jennifer G. Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, 274–283.

2. Anish Athalye , Logan Engstrom , Andrew Ilyas , and Kevin Kwok . 2018 . Synthesizing Robust Adversarial Examples . In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan , Stockholm, Sweden , July 10-15, 2018, Jennifer G. Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, 284–293. Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2018. Synthesizing Robust Adversarial Examples. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, Jennifer G. Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, 284–293.

3. Quantitative Verification of Neural Networks and Its Security Applications

4. EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

5. ZOO

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