In Pursuit of Preserving the Fidelity of Adversarial Images
Joseph Clements, Yingjie Lao · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
Adversarial examples have emerged as a severe concern for the security of neural networks. However, the ℓp-distances, typically used as a similarity constraint, often fail to capture human perceived similarity. Under challenging scenarios, such as attacking a defended model, this discrepancy leads to the severe degradation of image fidelity. In this paper, we find adversarial examples that better match the natural distribution of the input domain by integrating signal processing techniques into the attack framework, dynamically altering the allowed perturbation with a Rule Adjustable Distance (RADρ). The framework allows us to easily in-corporate structural similarity, Otsu’s method, or variance filtering to increase the fidelity of adversarial images while still adhering to an ℓp-bound.