Adversarial attacks hidden in plain sight
This addresses the issue of adversarial vulnerability in neural networks for security-critical applications, though it is incremental as it builds on existing attack and defense methods.
The paper tackles the problem of adversarial attacks on convolutional neural networks by developing a technique that hides these attacks in visually complex regions, making them imperceptible to humans. A user study validated the concealment, showing significant evidence of its effectiveness.
Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception.