Error Diffusion Halftoning Against Adversarial Examples
This addresses the security issue of adversarial attacks for deep learning systems, but it is incremental as it builds on existing image transformation and adversarial training methods.
The paper tackles the problem of adversarial examples fooling deep neural networks by proposing a new image transformation defense using error diffusion halftoning, combined with adversarial training, which improves adversarial robustness under advanced adaptive attacks while maintaining acceptable image quality.
Adversarial examples contain carefully crafted perturbations that can fool deep neural networks (DNNs) into making wrong predictions. Enhancing the adversarial robustness of DNNs has gained considerable interest in recent years. Although image transformation-based defenses were widely considered at an earlier time, most of them have been defeated by adaptive attacks. In this paper, we propose a new image transformation defense based on error diffusion halftoning, and combine it with adversarial training to defend against adversarial examples. Error diffusion halftoning projects an image into a 1-bit space and diffuses quantization error to neighboring pixels. This process can remove adversarial perturbations from a given image while maintaining acceptable image quality in the meantime in favor of recognition. Experimental results demonstrate that the proposed method is able to improve adversarial robustness even under advanced adaptive attacks, while most of the other image transformation-based defenses do not. We show that a proper image transformation can still be an effective defense approach. Code: https://github.com/shaoyuanlo/Halftoning-Defense