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In adversarial attacks intended to confound deep learning models, moststudies have focused on limiting the magnitude of the modification so thathumans do not notice the attack. On the other hand, during an attack againstautonomous cars, for example, most drivers would not find it strange if a smallinsect image were placed on a stop sign, or they may overlook it. In thispaper, we present a systematic approach to generate natural adversarialexamples against classification models by employing such natural-appearingperturbations that imitate a certain object or signal. We first show thefeasibility of this approach in an attack against an image classifier byemploying generative adversarial networks that produce image patches that havethe appearance of a natural object to fool the target model. We also introducean algorithm to optimize placement of the perturbation in accordance with theinput image, which makes the generation of adversarial examples fast and likelyto succeed. Moreover, we experimentally show that the proposed approach can beextended to the audio domain, for example, to generate perturbations that soundlike the chirping of birds to fool a speech classifier.

Generate (non-software) Bugs to Fool Classifiers