  Object detection systems using deep learning models have become increasingly
popular in robotics thanks to the rising power of CPUs and GPUs in embedded
systems. However, these models are susceptible to adversarial attacks. While
some attacks are limited by strict assumptions on access to the detection
system, we propose a novel hardware attack inspired by Man-in-the-Middle
attacks in cryptography. This attack generates a Universal Adversarial
Perturbations (UAP) and injects the perturbation between the USB camera and the
detection system via a hardware attack. Besides, prior research is misled by an
evaluation metric that measures the model accuracy rather than the attack
performance. In combination with our proposed evaluation metrics, we
significantly increased the strength of adversarial perturbations. These
findings raise serious concerns for applications of deep learning models in
safety-critical systems, such as autonomous driving.
