IAM

30thMARCH2020

READING

Danny Karmon, Daniel Zoran, Yoav Goldberg. LaVAN: Localized and Visible Adversarial Noise. ICML 2018: 2512-2520.

Karmon et al. propose a gradient-descent based method for obtaining adversarial patch like localized adversarial examples. In particular, after selecting a region of the image to be modified, several iterations of gradient descent are run in order to maximize the probability of the target class and simultaneously minimize the probability in the true class. After each iteration, the perturbation is masked to the patch and projected onto the valid range of [0,1] for images. On ImageNet, the authors show that these adversarial examples are effective against a normal, undefended network.

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: