REAP: A Large-Scale Realistic Adversarial Patch Benchmark
Nabeel Hingun, Chawin Sitawarin, Jerry Li, David Wagner

TL;DR
The REAP benchmark provides a realistic digital platform to evaluate adversarial patch attacks on traffic signs, revealing that such attacks may be less threatening in real-world scenarios than previously thought.
Contribution
This work introduces the REAP benchmark, enabling large-scale, realistic evaluation of adversarial patches on traffic signs using real images and transformations.
Findings
Adversarial patch attacks may be less effective in real-world conditions.
Success rates in digital simulations do not reliably predict real-world attack effectiveness.
REAP benchmark is publicly available for further research.
Abstract
Machine learning models are known to be susceptible to adversarial perturbation. One famous attack is the adversarial patch, a sticker with a particularly crafted pattern that makes the model incorrectly predict the object it is placed on. This attack presents a critical threat to cyber-physical systems that rely on cameras 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…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Generative Adversarial Networks and Image Synthesis
