  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.
