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Poster
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu · Wooyeong Jang · Jiefeng Chen · Lingjiao Chen · Somesh Jha

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #54
in Posters Thu »
In this paper we study leveraging \emph{confidence information} induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model. A natural measure of confidence is $\|F(\bfx)\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by analyzing an adversarial training formulation proposed by Madry et al.. We demonstrate that, under a variety of instantiations, an only somewhat good solution to their objective induces confidence to be a discriminator, which can distinguish between right and wrong model predictions in a neighborhood of a point sampled from the underlying distribution. Based on this, we propose Highly Confident Near Neighbor ($\HCNN$), a framework that combines confidence information and nearest neighbor search, to reinforce adversarial robustness of a base model. We give algorithms in this framework and perform a detailed empirical study. We report encouraging experimental results that support our analysis, and also discuss problems we observed with existing adversarial training.
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Author Information

Xi Wu (Google)

Completed my PhD in Computer Science from UW-Madison, advised by Jeffrey F. Naughton and Somesh Jha. Now a software engineer at Google. [Google PhD Fellow 2016 in privacy and security](https://ai.googleblog.com/2016/03/announcing-2016-google-phd-fellows-for.html).

Wooyeong Jang (University of Wisconsin - Madison)
Jiefeng Chen (University of Wisconsin-Madison)
Lingjiao Chen (University of Wisconsin-Madison)
Somesh Jha (University of Wisconsin, Madison)

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