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Enhancing the Transferability of Adversarial Examples with Random Patch
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples.
Yaoyuan Zhang +5 more
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Naturalistic Physical Adversarial Patch for Object Detectors
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021Most 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 pro-pose a method to
Hu, Y.-C.-T. +5 more
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2017
Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
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Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
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Scaling Resilient Adversarial Patch
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021Yunhong Yin +4 more
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Visually imperceptible adversarial patch attacks
Computers & Security, 2022Yaguan Qian +6 more
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Universal Adversarial Patch Attack for Automatic Checkout Using Perceptual and Attentional Bias
IEEE Transactions on Image Processing, 2022Jiakai Wang, Aishan Liu, Xiao Bai
exaly
Simultaneously Optimizing Perturbations and Positions for Black-Box Adversarial Patch Attacks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Xingxing Wei, Ying Guo
exaly
Transferable Black-Box Attack Against Face Recognition With Spatial Mutable Adversarial Patch
IEEE Transactions on Information Forensics and Security, 2023Haotian Ma, Ke Xu, Xinghao Jiang
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Towards a physical-world adversarial patch for blinding object detection models
Information Sciences, 2021Yajie Wang, Yu-An Tan, Quanxin Zhang
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