A Man-in-the-Middle Attack against Object Detection Systems

Han Wu, Sareh Rowlands, Johan Wahlstrom

Thanks to the increasing power of CPUs and GPUs in embedded systems, deep-learning-enabled object detection systems have become pervasive in a multitude of robotic applications. While deep learning models are vulnerable to several well-known adversarial attacks, the applicability of these attacks is severely limited by strict assumptions on, for example, access to the detection system. Inspired by Man-in-the-Middle attacks in cryptography, we propose a novel hardware attack on object detection systems that overcomes these limitations. Experiments prove that it is possible to generate an efficient Universal Adversarial Perturbation (UAP) within one minute and then use the perturbation to attack a detection system via the Man-in-the-Middle attack. These findings raise serious concerns for applications of deep learning models in safety-critical systems, such as autonomous driving. Demo Video: https://youtu.be/OvIpe-R3ZS8.

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