Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search

Author:

Xia Pengfei1ORCID,Li Ziqiang1ORCID,Li Bin1ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference57 articles.

1. Sravanti Addepalli Samyak Jain Gaurang Sriramanan and Venkatesh Babu Radhakrishnan. 2021. Towards Achieving Adversarial Robustness Beyond Perceptual Limits. (2021).

2. Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018. Generating natural language adversarial examples. arXiv preprint arXiv:1804.07998 (2018).

3. The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations

4. Anish Athalye, Nicholas Carlini, and David Wagner. 2018. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In International conference on machine learning. PMLR, 274--283.

5. Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin. 2019. On evaluating adversarial robustness. arXiv preprint arXiv:1902.06705 (2019).

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