  Recent studies have shown that Adversarial Patches (APs) can effectively
manipulate object detection models. However, the conspicuous patterns often
associated with these patches tend to attract human attention, posing a
significant challenge. Existing research has primarily focused on enhancing
attack efficacy in the physical domain while often neglecting the optimization
of stealthiness and transferability. Furthermore, applying APs in real-world
scenarios faces major challenges related to transferability, stealthiness, and
practicality. To address these challenges, we introduce generalization theory
into the context of APs, enabling our iterative process to simultaneously
enhance transferability and refine visual correlation with realistic images. We
propose a Dual-Perception-Based Framework (DPBF) to generate the More Vivid
Patch (MVPatch), which enhances transferability, stealthiness, and
practicality. The DPBF integrates two key components: the
Model-Perception-Based Module (MPBM) and the Human-Perception-Based Module
(HPBM), along with regularization terms. The MPBM employs ensemble strategy to
reduce object confidence scores across multiple detectors, thereby improving AP
transferability with robust theoretical support. Concurrently, the HPBM
introduces a lightweight method for achieving visual similarity, creating
natural and inconspicuous adversarial patches without relying on additional
generative models. The regularization terms further enhance the practicality of
the generated APs in the physical domain. Additionally, we introduce
naturalness and transferability scores to provide an unbiased assessment of
APs. Extensive experimental validation demonstrates that MVPatch achieves
superior transferability and a natural appearance in both digital and physical
domains, underscoring its effectiveness and stealthiness.
