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Many physical adversarial patch generation methods are widely proposed toprotect personal privacy from malicious monitoring using object detectors.However, they usually fail to generate satisfactory patch images in terms ofboth stealthiness and attack performance without making huge efforts on carefulhyperparameter tuning. To address this issue, we propose a novel naturalisticadversarial patch generation method based on the diffusion models (DM). Throughsampling the optimal image from the DM model pretrained upon natural images, itallows us to stably craft high-quality and naturalistic physical adversarialpatches to humans without suffering from serious mode collapse problems asother deep generative models. To the best of our knowledge, we are the first topropose DM-based naturalistic adversarial patch generation for objectdetectors. With extensive quantitative, qualitative, and subjectiveexperiments, the results demonstrate the effectiveness of the proposed approachto generate better-quality and more naturalistic adversarial patches whileachieving acceptable attack performance than other state-of-the-art patchgeneration methods. We also show various generation trade-offs under differentconditions.

Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector