PapMOT: Exploring Adversarial Patch Attack against Multiple Object Tracking
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
Tracking multiple objects in a continuous video stream is crucial for many
computer vision tasks. It involves detecting and associating objects with their
respective identities across successive frames. Despite significant progress
made in multiple object tracking (MOT), recent studies have revealed the
vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all
of these attacks belong to digital attacks that inject pixel-level noise into
input images, and are therefore ineffective in physical scenarios. To fill this
gap, we propose PapMOT, which can generate physical adversarial patches against
MOT for both digital and physical scenarios. Besides attacking the detection
mechanism, PapMOT also optimizes a printable patch that can be detected as new
targets to mislead the identity association process. Moreover, we introduce a
patch enhancement strategy to further degrade the temporal consistency of
tracking results across video frames, resulting in more aggressive attacks. We
further develop new evaluation metrics to assess the robustness of MOT against
such attacks. Extensive evaluations on multiple datasets demonstrate that our
PapMOT can successfully attack various architectures of MOT trackers in digital
scenarios. We also validate the effectiveness of PapMOT for physical attacks by
deploying printed adversarial patches in the real world.
