MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World
CoRR(2023)
摘要
Recent investigations demonstrate that adversarial patches can be utilized to
manipulate the result of object detection models. However, the conspicuous
patterns on these patches may draw more attention and raise suspicions among
humans. Moreover, existing works have primarily focused on enhancing the
efficacy of attacks in the physical domain, rather than seeking to optimize
their stealth attributes and transferability potential. To address these
issues, we introduce a dual-perception-based attack framework that generates an
adversarial patch known as the More Vivid Patch (MVPatch). The framework
consists of a model-perception degradation method and a human-perception
improvement method. To derive the MVPatch, we formulate an iterative process
that simultaneously constrains the efficacy of multiple object detectors and
refines the visual correlation between the generated adversarial patch and a
realistic image. Our method employs a model-perception-based approach that
reduces the object confidence scores of several object detectors to boost the
transferability of adversarial patches. Further, within the
human-perception-based framework, we put forward a lightweight technique for
visual similarity measurement that facilitates the development of inconspicuous
and natural adversarial patches and eliminates the reliance on additional
generative models. Additionally, we introduce the naturalness score and
transferability score as metrics for an unbiased assessment of various
adversarial patches' natural appearance and transferability capacity. Extensive
experiments demonstrate that the proposed MVPatch algorithm achieves superior
attack transferability compared to similar algorithms in both digital and
physical domains while also exhibiting a more natural appearance. These
findings emphasize the remarkable stealthiness and transferability of the
proposed MVPatch attack algorithm.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要