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Robust Physical-World Attacks on Deep Learning Models

Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, Dawn Song

Although deep neural networks (DNNs) perform well in a variety of applications, they are vulnerable to adversarial examples resulting from small-magnitude perturbations added to the input data. Inputs modified in this way can be mislabeled as a target class in targeted attacks or as a random class different from the ground truth in untargeted attacks. However, recent studies have demonstrated that such adversarial examples have limited effectiveness in the physical world due to changing physical conditions--they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper, we propose a general attack algorithm--Robust Physical Perturbations (RP2)-- that takes into account the numerous physical conditions and produces robust adversarial perturbations. Using a real-world example of road sign recognition, we show that adversarial examples generated using RP2 achieve high attack success rates in the physical world under a variety of conditions, including different viewpoints. Furthermore, to the best of our knowledge, there is currently no standardized way to evaluate physical adversarial perturbations. Therefore, we propose a two-stage evaluation methodology and tailor it to the road sign recognition use case. Our methodology captures a range of diverse physical conditions, including those encountered when images are captured from moving vehicles. We evaluate our physical attacks using this methodology and effectively fool two road sign classifiers. Using a perturbation in the shape of black and white stickers, we attack a real Stop sign, causing targeted misclassification in 100% of the images obtained in controlled lab settings and above 84% of the captured video frames obtained on a moving vehicle for one of the classifiers we attack.

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