Results 11 to 20 of about 29,955 (264)
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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Smooth adversarial examples [PDF]
EURASIP Journal on Information Security, 2020 AbstractThis paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally
Zhang, Hanwei +3 more
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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 perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and
Du, Zhenyu, Liu, Fangzheng, Yan, Xuehu
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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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Verifying the Causes of Adversarial Examples [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial ...
Li, H +4 more
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Efficient Adversarial Training With Transferable Adversarial Examples [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Haizhong Zheng +4 more
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Adversarial examples for models of code [PDF]
Proceedings of the ACM on Programming Languages, 2020 Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulnerable to adversarial examples , and introduce a novel approach for attacking trained models of code using ...
Noam Yefet, Uri Alon 0002, Eran Yahav
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Generating Adversarial Examples with Adversarial Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Chaowei Xiao +5 more
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Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
2022 IEEE International Conference on Image Processing (ICIP), 2022 Appeared in ICIP ...
Jie Zhang 0081 +3 more
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Multi-target Category Adversarial Example Generating Algorithm Based on GAN [PDF]
Jisuanji kexue, 2022 Although deep neural networks perform well in many areas,research shows that deep neural networks are vulnerable to attacks from adversarial examples.There are many algorithms for attacking neural networks,but the attack speed of most attack algorithms ...
LI Jian, GUO Yan-ming, YU Tian-yuan, WU Yu-lun, WANG Xiang-han, LAO Song-yang
doaj +1 more source