2018

CAAD 2018: Generating Transferable Adversarial Examples

Sharma, Yash, Le, Tien-Dung, Alzantot, Moustafa

Understand

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations carefully crafted to fool the targeted DNN, in both the non-targeted and targeted case.

In the non-targeted case, the attacker simply aims to induce misclassification.
In the targeted case, the attacker aims to induce classification to a specified target class.
In addition, it has been observed that strong adversarial examples can transfer to unknown models, yielding a serious security concern.

Built on

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Similar

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Boosting adversarial attacks with momentum
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Then

Genattack: Practical black-box attacks with gradient-free optimization
Original
M. Alzantot, Y. Sharma, S. Chakraborty, and M. Srivastava · 2018
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Generating natural language adversarial examples
M. Alzantot, Y. Sharma, A. Elgohary, B. Ho, M. Srivastava, and K. Chang · 2018
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On the robustness of the cvpr 2018 white-box adversarial example defenses
Original
A. Athalye and N. Carlini · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Original
A. Athalye, N. Carlini, and D. Wagner · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Original
N. Carlini and D. Wagner · 2018
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Adversarial attacks and defences competition
Original
A. Kurakin, I. Goodfellow, S. Bengio, Y. Dong, F. Liao, M. Liang, T. Pang, J. Zhu, X. Hu, C. Xie, J. Wang, Z. Zhang, Z. Ren, A. Yuille, S. Huang, Y. Zhao, Y. Zhao, Z. Han, J Long, Y. Berdibekov, T. Akiba, S. Tokui, and M. Abe · 2018
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Bypassing feature squeezing by increasing adversary strength
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Y. Sharma and P. Y. Chen · 2018
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There is no free lunch in adversarial robustness (but there are unexpected benefits)
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D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2018
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Improving transferability of adversarial examples with input diversity
Original
C. Xie, Z. Zhang, J. Wang, Y. Zhou, Z. Ren, and A. Yuille · 2018
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