IAM

04thOCTOBER2019

READING

Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh. The Limitations of Adversarial Training and the Blind-Spot Attack. CoRR abs/1901.04684 (2019).

Zhang et al. search for “blind spots” in the data distribution and show that blind spot test examples can be used to find adversarial examples easily. On MNIST, the data distribution is approximated using kernel density estimation were the distance metric is computed in dimensionality-reduced feature space (of an adversarially trained model). For dimensionality reduction, t-SNE is used. Blind spots are found by slightly shifting pixels or changing the gray value of the background. Based on these blind spots, adversarial examples can easily be found for MNIST and Fashion-MNIST.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: