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Adversarial Examples for Malware Detection
2017Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse +4 more
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Adversarial example detection based on saliency map features
Applied intelligence (Boston), 2021Shen Wang, Yuxin Gong
semanticscholar +1 more source
Advops: Decoupling Adversarial Examples
Pattern Recognition, 2023Donghua Wang 0001 +3 more
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Unauthorized AI cannot recognize me: Reversible adversarial example
Pattern Recognition, 2023Weiming Zhang +2 more
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Interpreting Universal Adversarial Example Attacks on Image Classification Models
IEEE Transactions on Dependable and Secure Computing, 2023Yi Ding, Fuyuan Tan, Ji Geng
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Model scheduling and sample selection for ensemble adversarial example attacks
Pattern Recognition, 2022Wei Yuan
exaly
Adversarial example detection using semantic graph matching
Applied Soft Computing Journal, 2023Yuxin Gong, Shen Wang, Xunzhi Jiang
exaly
ADS-detector: An attention-based dual stream adversarial example detection method
Knowledge-Based Systems, 2023Sensen Guo, Peican Zhu
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Adversarial Examples with Specular Highlights
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023Vanshika Vats, Koteswar Rao Jerripothula
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Training generative adversarial networks by auxiliary adversarial example regulator
Applied Soft Computing Journal, 2023Yan Gan
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