Modality Unified Attack for Omni-Modality Person Re-Identification
Journal:
arXiv
Published Date:
Jan 22, 2025
Abstract
Deep learning based person re-identification (re-id) models have been widely
employed in surveillance systems. Recent studies have demonstrated that
black-box single-modality and cross-modality re-id models are vulnerable to
adversarial examples (AEs), leaving the robustness of multi-modality re-id
models unexplored. Due to the lack of knowledge about the specific type of
model deployed in the target black-box surveillance system, we aim to generate
modality unified AEs for omni-modality (single-, cross- and multi-modality)
re-id models. Specifically, we propose a novel Modality Unified Attack method
to train modality-specific adversarial generators to generate AEs that
effectively attack different omni-modality models. A multi-modality model is
adopted as the surrogate model, wherein the features of each modality are
perturbed by metric disruption loss before fusion. To collapse the common
features of omni-modality models, Cross Modality Simulated Disruption approach
is introduced to mimic the cross-modality feature embeddings by intentionally
feeding images to non-corresponding modality-specific subnetworks of the
surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is
devised to facilitate the attacker to comprehensively corrupt the informative
content of person images by leveraging a multi modality feature collaborative
metric disruption loss. Extensive experiments show that our MUA method can
effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%,
49.0% and 62.7% mean mAP Drop Rate, respectively.
