Man-in-the-Middle Attack against Object Detection Systems
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Is deep learning secure for robots? As embedded systems have access to more powerful CPUs and GPUs, deep-learning-enabled object detection systems become pervasive in robotic applications. Meanwhile, prior research unveils that deep learning models are vulnerable to adversarial attacks. Does this put real-world robots at threat? Our research borrows the idea of the Main-in-the-Middle attack from Cryptography to attack an object detection system. Our experimental results prove that we can generate a strong Universal Adversarial Perturbation (UAP) within one minute and then use the perturbation to attack a detection system via the Man-in-the-Middle attack. Our findings raise a serious concern over the applications of deep learning models in safety-critical systems such as autonomous driving.
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