Robust Physical-World Attacks on Deep Learning Models

27 Jul 2017Kevin EykholtIvan EvtimovEarlence FernandesBo LiAmir RahmatiChaowei XiaoAtul PrakashTadayoshi KohnoDawn Song

Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations.Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms... (read more)

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