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On the Salience of Adversarial Examples
2019Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. Countermeasures against these attacks that take a global approach tend to be lossy to the original data ...
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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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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, 2023Jun Sakuma +2 more
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Adversarial example detection using semantic graph matching
Applied Soft Computing Journal, 2023Shen Wang, Xunzhi Jiang, Yuxin Gong
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Model scheduling and sample selection for ensemble adversarial example attacks
Pattern Recognition, 2022Wei Yuan
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Adversarial-Example Attacks Toward Android Malware Detection System
IEEE Systems Journal, 2020Heng Li, Wei Yuan, Henry Leung
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ADS-detector: An attention-based dual stream adversarial example detection method
Knowledge-Based Systems, 2023Peican Zhu, Sensen Guo
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Training generative adversarial networks by auxiliary adversarial example regulator
Applied Soft Computing Journal, 2023Yan Gan, Yiguang Liu
exaly
Rethinking Adversarial Examples in Wargames
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022openaire +1 more source