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Physical world adversarial attack is a highly practical and threateningattack, which fools real world deep learning systems by generating conspicuousand maliciously crafted real world artifacts. In physical world attacks,evaluating naturalness is highly emphasized since human can easily detect andremove unnatural attacks. However, current studies evaluate naturalness in acase-by-case fashion, which suffers from errors, bias and inconsistencies. Inthis paper, we take the first step to benchmark and assess visual naturalnessof physical world attacks, taking autonomous driving scenario as the firstattempt. First, to benchmark attack naturalness, we contribute the firstPhysical Attack Naturalness (PAN) dataset with human rating and gaze. PANverifies several insights for the first time: naturalness is (disparately)affected by contextual features (i.e., environmental and semantic variations)and correlates with behavioral feature (i.e., gaze signal). Second, toautomatically assess attack naturalness that aligns with human ratings, wefurther introduce Dual Prior Alignment (DPA) network, which aims to embed humanknowledge into model reasoning process. Specifically, DPA imitates humanreasoning in naturalness assessment by rating prior alignment and mimics humangaze behavior by attentive prior alignment. We hope our work fosters researchesto improve and automatically assess naturalness of physical world attacks. Ourcode and dataset can be found at https://github.com/zhangsn-19/PAN.

Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks