https://arxiv.org/abs/1801.02608
> 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. -- ☣ uǝlƃ ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove
