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Poster
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
Moustapha Cisse · Yossi Adi · Natalia Neverova · Joseph Keshet

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #137
in Posters Tue »

Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.

Keywords: Deep Learning · Adversarial Networks · Privacy, Anonymity, and Security
[ Paper] 

Author Information

Moustapha Cisse (Facebook AI Research)
Yossi Adi (Bar Ilan University)
Natalia Neverova (Facebook AI Research)
Joseph Keshet (Bar-Ilan University)

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