IAM

Check out our latest research on adversarial robustness and generalization of deep networks.
27thJULY2018

READING

Weilin Xu, David Evans, Yanjun Qi. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. CoRR abs/1704.01155, 2017.

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.

Figure 1: Illustration of using squeezing for adversarial example detection.

Also find this summary on ShortScience.org.

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