Synthesizing Robust Adversarial Examples

Neural networks are susceptible to adversarial examples: small, carefully-crafted perturbations can cause networks to misclassify inputs in arbitrarily chosen ways. However, some studies have showed that adversarial examples crafted following the usual methods are not tolerant to small transformations: for example, zooming in on an adversarial image can cause it to be classified correctly again. This raises the question of whether adversarial examples are a concern in practice, because many real-world systems capture images from multiple scales and perspectives. This paper shows that adversarial examples can be made robust to distributions of transformations. Our approach produces single images that are simultaneously adversarial under all transformations in a chosen distribution, showing that we cannot rely on transformations such as rescaling, translation, and rotation to protect against adversarial examples.