Results 1 to 10 of about 16,674 (259)
Minimum Adversarial Examples [PDF]
Entropy, 2022 Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting ...
Zhenyu Du, Fangzheng Liu, Xuehu Yan
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Smooth adversarial examples [PDF]
EURASIP Journal on Information Security, 2020 This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts.
Hanwei Zhang +3 more
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Targeted Universal Adversarial Examples for Remote Sensing
Remote Sensing, 2022 Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples.
Tao Bai, Hao Wang, Bihan Wen
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Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks
IEEE Access, 2020 Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree ...
Xueqin Zhang +4 more
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Understanding adversarial robustness against on-manifold adversarial examples
Pattern RecognitionDeep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of the adversarial examples is the off-manifold assumption: adversarial examples lie off the data manifold. However, recent research showed
Yanbo Fan, Zhi-Quan Luo
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Adversarial Examples Detection Method Based on Image Denoising and Compression [PDF]
Jisuanji gongcheng, 2023 Numerous deep learning achievements in the field of computer vision have been widely applied in real life. However, adversarial examples can lead to false positives in deep learning models with high confidence, resulting in serious security consequences.
Feiyu WANG, Fan ZHANG, Jiayu DU, Hongle LEI, Xiaofeng QI
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Natural Adversarial Examples [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that ...
Dan Hendrycks +4 more
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Survey of Image Adversarial Example Defense Techniques [PDF]
Jisuanji kexue yu tansuo, 2023 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots.
LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu
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Distinguishability of adversarial examples [PDF]
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020 Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose
Yi Qin, Ryan Hunt, Chuan Yue
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Fooling Examples: Another Intriguing Property of Neural Networks
Sensors, 2023 Neural networks have been proven to be vulnerable to adversarial examples; these are examples that can be recognized by both humans and neural networks, although neural networks give incorrect predictions.
Ming Zhang, Yongkang Chen, Cheng Qian
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