Can You Trust What You See? Alpha Channel No-Box Attacks on Video Object Detection
Ariana Yi, Ce Zhou, Liyang Xiao, Qiben Yan

TL;DR
This paper introduces {}-Cloak, a novel no-box adversarial attack on video object detection that exploits the alpha channel to create visually stealthy yet highly effective attacks, exposing a new vulnerability in video perception systems.
Contribution
It presents the first alpha channel-based no-box attack on video object detectors, demonstrating its effectiveness and revealing a new security vulnerability.
Findings
Achieved 100% attack success rate across multiple detectors.
Exposed alpha channel as a previously overlooked attack surface.
Demonstrated the attack's stealthiness and broad applicability.
Abstract
As object detection models are increasingly deployed in cyber-physical systems such as autonomous vehicles (AVs) and surveillance platforms, ensuring their security against adversarial threats is essential. While prior work has explored adversarial attacks in the image domain, those attacks in the video domain remain largely unexamined, especially in the no-box setting. In this paper, we present {\alpha}-Cloak, the first no-box adversarial attack on object detectors that operates entirely through the alpha channel of RGBA videos. {\alpha}-Cloak exploits the alpha channel to fuse a malicious target video with a benign video, resulting in a fused video that appears innocuous to human viewers but consistently fools object detectors. Our attack requires no access to model architecture, parameters, or outputs, and introduces no perceptible artifacts. We systematically study the support for…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Neural Network Applications · Digital Media Forensic Detection
