On Evaluating Adversarial Robustness

On Evaluating Adversarial Robustness. Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I., Madry, A., & Kurakin, A. February, 2019. arXiv:1902.06705 [cs, stat]

Paper abstract bibtex

Correctly evaluating defenses against adversarial examples has proven to be extremely diﬃcult. Despite the signiﬁcant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect.

@misc{carlini_evaluating_2019,
	title = {On {Evaluating} {Adversarial} {Robustness}},
	url = {http://arxiv.org/abs/1902.06705},
	abstract = {Correctly evaluating defenses against adversarial examples has proven to be extremely diﬃcult. Despite the signiﬁcant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect.},
	language = {en},
	urldate = {2024-06-18},
	publisher = {arXiv},
	author = {Carlini, Nicholas and Athalye, Anish and Papernot, Nicolas and Brendel, Wieland and Rauber, Jonas and Tsipras, Dimitris and Goodfellow, Ian and Madry, Aleksander and Kurakin, Alexey},
	month = feb,
	year = {2019},
	note = {arXiv:1902.06705 [cs, stat]},
	keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Jab/\#Pre, Statistics - Machine Learning},
}

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