Sample Based Fast Adversarial Attack Method

Author:

Wang Zhi-MingORCID,Gu Meng-Ting,Hou Jia-Hui

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Reference17 articles.

1. Hosseini H, Xiao B, Poovendran R (2017) Google’s cloud vision API is not robust to noise. In: 16th IEEE international conference on machine learning and applications (ICMLA)

2. Szegedy C, Zaremba W, Sutskever I et al (2014) Intriguing properties of neural networks. In: International conference on learning representations (ICLR)

3. Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. In: Proceedings of the international conference on learning representations (ICLR)

4. Rozsa A, Rudd EM, Boult TE (2016) Adversarial diversity and hard positive generation. In: IEEE conference on computer vision and pattern recognition (CVPR) workshops

5. Kurakin A, Goodfellow IJ, Bengio S (2019) Adversarial examples in the physical world. https://arxiv.org/abs/1607.02533v4 . Accessed 11 Apr 2019

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2. Benford’s law: What does it say on adversarial images?;Journal of Visual Communication and Image Representation;2023-05

3. Eigenpatches—Adversarial Patches from Principal Components;Advances in Visual Computing;2023

4. A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions;Computers & Security;2022-10

5. Benford's Law: What Does it Say on Adversarial Images?;SSRN Electronic Journal;2022

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