PNAP-YOLO: An Improved Prompts-Based Naturalistic Adversarial Patch Model for Object Detectors

Detectors have been extensively utilized in various scenarios such as autonomous driving and video surveillance. Nonetheless, recent studies have revealed that these detectors are vulnerable to adversarial attacks, particularly adversarial patch attacks. Adversarial patches are specifically crafted to disrupt deep learning models by disturbing image regions, thereby misleading the deep learning models when added to into normal images. Traditional adversarial patches often lack semantics, posing challenges in maintaining concealment in physical world scenarios. To tackle this issue, this paper proposes a Prompt-based Natural Adversarial Patch generation method, which creates patches controllable by textual descriptions to ensure flexibility in application. This approach leverages the latest text-to-image generation model—Latent Diffusion Model (LDM) to produce adversarial patches. We optimize the attack performance of the patches by updating the latent variables of LDM through a combined loss function. Experimental results indicate that our method can generate more natural, semantically rich adversarial patches, achieving effective attacks on various detectors.