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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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ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches
Pattern Recognition, 2023Ambra Demontis +2 more
exaly
Inconspicuous Adversarial Patches for Fooling Image-Recognition Systems on Mobile Devices
IEEE Internet of Things Journal, 2022Tao Bai, Jun Zhao
exaly
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
IEEE Transactions on Information Forensics and Security, 2022Bao Gia Doan, Minhui Xue, Shiqing Ma
exaly
Infrared Adversarial Patches with Learnable Shapes and Locations in the Physical World
International Journal of Computer Vision, 2023Xingxing Wei, Yao Huang
exaly