Results 21 to 30 of about 173,113 (165)
Adversarial Examples-Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices. [PDF]
IEEE Internet Things J, 2021 Rahman A +3 more
europepmc +2 more sources
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 ...
Hendrycks, Dan +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
doaj +1 more source
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.
Zheng, Haizhong +4 more
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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
doaj +1 more source
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
2022 IEEE International Conference on Image Processing (ICIP), 2022 Appeared in ICIP ...
Zhang, Jie +3 more
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FADER: Fast adversarial example rejection [PDF]
Neurocomputing, 2022 Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from legitimate training samples at different layer representations - a behavior normally exhibited by adversarial ...
Crecchi, Francesco +4 more
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Adversarial Examples Generation Method Based on Image Color Random Transformation [PDF]
Jisuanji kexue, 2023 Although deep neural networks(DNNs) have good performance in most classification tasks,they are vulnerable to adversarial examples,making the security of DNNs questionable.Research designs to generate strongly aggressive adversarial examples can help ...
BAI Zhixu, WANG Hengjun, GUO Kexiang
doaj +1 more source
Semantic Adversarial Examples [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error.
Hosseini, Hossein, Poovendran, Radha
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Adversarial Examples: Opportunities and Challenges [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 16 pages, 13 figures, 5 ...
Jiliang Zhang, Chen Li
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