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Workshop: Workshop on Machine Learning Safety

REAP: A Large-Scale Realistic Adversarial Patch Benchmark

Nabeel Hingun · Chawin Sitawarin · Jerry Li · David Wagner


Machine learning models are known to be susceptible to adversarial perturbation. One famous attack is the adversarial patch, a sticker with a crafted pattern that makes the model incorrectly predict the object it is placed on. This attack presents a critical threat to cyber-physical systems such as autonomous cars. Despite the significance of the problem, conducting research in this setting has been difficult; evaluating attacks and defenses in the real world is exceptionally costly while synthetic data are unrealistic. In this work, we propose the REAP (REalistic Adversarial Patch) Benchmark, a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, our benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign, while matching real-world conditions. Using our benchmark, we perform the first large-scale assessments of adversarial patch attacks under realistic conditions. We release our benchmark publicly at

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