Key points are not available for this paper at this time.
Studies have proven that applying patch stickers generated through adversarial training to target objects can effectively deceive classifiers or target detectors. These 'Print-and-paste' adversarial attacks however have three shortcomings. First, touching the target object physically is required, which may be infeasible in practice. Second, stickers might be taken as evidence to identify attackers. Third, the attack effect decreases significantly in poor light, especially at long distances. To overcome above limitations, we introduce OptiCloak, a car vanishing attack, which fools the Object Detector (OD) of a vision-based autonomous driving systems with transient projection pattern. We establish three digital-to-physical mapping models to compensate the distortions caused by perspective deformation, double image and partial light reflection in real-world. Furthermore, to avoid adversarial functionality degeneration caused by the loss of patch details in long-range attacks, we utilize MeanShift Filtering to constrain the 'resolution' of pixels in a patch during training. We propose a gradient-free patch updating approach, which utilizes ZO-AdaMM to approximate gradients and model parameters through confidence scores of OD, making OptiCloak can work well in both white-box and black-box scenarios. We deploy OptiCloak in real-world driving scenarios, and the extensive experimental results demonstrate that OptiCloak achieves similar Attack Success Rates (ASRs) as printed patches in bright environments, while significantly improving the attack performance in gloomy environments. This effect is validated across all settings, including different angles, imaging devices, and film transparency rates. In black-box settings, the average ASR can reach 71%, with a maximum attack distance of approximately 10m.
Wen et al. (Fri,) studied this question.