Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based
  on Diffusion Model for Object Detector
Shuo-Yen Lin Ernie Chu Che-Hsien Lin Jun-Cheng Chen Jia-Ching Wang
Many physical adversarial patch generation methods are widely proposed to
protect personal privacy from malicious monitoring using object detectors.
However, they usually fail to generate satisfactory patch images in terms of
both stealthiness and attack performance without making huge efforts on careful
hyperparameter tuning. To address this issue, we propose a novel naturalistic
adversarial patch generation method based on the diffusion models (DM). Through
sampling the optimal image from the DM model pretrained upon natural images, it
allows us to stably craft high-quality and naturalistic physical adversarial
patches to humans without suffering from serious mode collapse problems as
other deep generative models. To the best of our knowledge, we are the first to
propose DM-based naturalistic adversarial patch generation for object
detectors. With extensive quantitative, qualitative, and subjective
experiments, the results demonstrate the effectiveness of the proposed approach
to generate better-quality and more naturalistic adversarial patches while
achieving acceptable attack performance than other state-of-the-art patch
generation methods. We also show various generation trade-offs under different
conditions.