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There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task.
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2018
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Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman, “Pixeldefend: Leveraging generative models to understand and defend against adversarial examples,” in Iclr , 2018
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J. Uesato, B. O’Donoghue, P. Kohli, and A. Oord, “Adversarial risk and the dangers of evaluating against weak attacks,” in International Conference on Machine Learning , 2018, pp. 5032–5041
2018
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2018
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2019
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