Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Paper summary Xu et al. propose feature squeezing for detecting and defending against adversarial examples. In particular, they consider “squeezing” the bit depth of the input images as well as local and non-local smoothing (Gaussian, median filtering etc.). In experiments they show that feature squeezing preserves accuracy while defending against adversarial examples. Figure 1 additionally shows an illustration of how feature squeezing can be used to detect adversarial examples. https://i.imgur.com/Ixv522J.png Figure 1: Illustration of using squeezing for adversarial example detection. Also find this summary on [davidstutz.de](https://davidstutz.de/category/reading/).
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu and David Evans and Yanjun Qi
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.CV, cs.CR, cs.LG

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Summary by David Stutz 1 month ago
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