Naturalistic Physical Adversarial Patch for Object Detectors

Yu-Chih-Tuan Hu, Bo-Han Kung, Daniel Stanley Tan, Jun-Cheng Chen, Kai-Lung Hua, Wen-Huang Cheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7848-7857


Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches. This leads to conspicuous and attention-grabbing patterns for the generated patches which can be easily identified by humans. To address this issue, we propose a method to craft physical adversarial patches for object detectors by leveraging the learned image manifold of a pretrained generative adversarial network (GAN) (e.g., BigGAN and StyleGAN) upon real-world images. Through sampling the optimal image from the GAN, our method can generate natural looking adversarial patches while maintaining high attack performance. With extensive experiments on both digital and physical domains and several independent subjective surveys, the results show that our proposed method produces significantly more realistic and natural looking patches than several state-of-the-art baselines while achieving competitive attack performance.

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@InProceedings{Hu_2021_ICCV, author = {Hu, Yu-Chih-Tuan and Kung, Bo-Han and Tan, Daniel Stanley and Chen, Jun-Cheng and Hua, Kai-Lung and Cheng, Wen-Huang}, title = {Naturalistic Physical Adversarial Patch for Object Detectors}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {7848-7857} }