LaVAN: Localized and Visible Adversarial Noise


Danny Karmon, Daniel Zoran, Yoav Goldberg ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2507-2515, 2018.


Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without covering any of the main object(s) in the image. We show that it is possible to generate localized adversarial noises that cover only 2% of the pixels in the image, none of them over the main object, and that are transferable across images and locations, and successfully fool a state-of-the-art Inception v3 model with very high success rates.

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