  Deep neural networks exhibit excellent performance in computer vision tasks,
but their vulnerability to real-world adversarial attacks, achieved through
physical objects that can corrupt their predictions, raises serious security
concerns for their application in safety-critical domains. Existing defense
methods focus on single-frame analysis and are characterized by high
computational costs that limit their applicability in multi-frame scenarios,
where real-time decisions are crucial.
  To address this problem, this paper proposes an efficient attention-based
defense mechanism that exploits adversarial channel-attention to quickly
identify and track malicious objects in shallow network layers and mask their
adversarial effects in a multi-frame setting. This work advances the state of
the art by enhancing existing over-activation techniques for real-world
adversarial attacks to make them usable in real-time applications. It also
introduces an efficient multi-frame defense framework, validating its efficacy
through extensive experiments aimed at evaluating both defense performance and
computational cost.
