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We propose a new real-world attack against the computer vision based systemsof autonomous vehicles (AVs). Our novel Sign Embedding attack exploits theconcept of adversarial examples to modify innocuous signs and advertisements inthe environment such that they are classified as the adversary's desiredtraffic sign with high confidence. Our attack greatly expands the scope of thethreat posed to AVs since adversaries are no longer restricted to justmodifying existing traffic signs as in previous work. Our attack pipelinegenerates adversarial samples which are robust to the environmental conditionsand noisy image transformations present in the physical world. We ensure thisby including a variety of possible image transformations in the optimizationproblem used to generate adversarial samples. We verify the robustness of theadversarial samples by printing them out and carrying out drive-by testssimulating the conditions under which image capture would occur in a real-worldscenario. We experimented with physical attack samples for different distances,lighting conditions and camera angles. In addition, extensive evaluations werecarried out in the virtual setting for a variety of image transformations. Theadversarial samples generated using our method have adversarial success ratesin excess of 95% in the physical as well as virtual settings.

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos