Results 11 to 20 of about 237,731 (274)
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
doaj +4 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
openaire +2 more sources
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
Zhenyu Du, Fangzheng Liu, Xuehu Yan
openaire +3 more sources
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +4 more sources
Perceptually Similar Image Classification Adversarial Example Generation Model
Jisuanji kexue yu tansuo, 2020 The existing generator-based adversarial example generation model can effectively reduce the construction time of an adversarial example compared to the algorithms based on iterative original image modification, but the obvious differences between ...
LI Junjie, WANG Qian
doaj +1 more source
Dual-Targeted Textfooler Attack on Text Classification Systems
IEEE Access, 2023 Deep neural networks provide good performance on classification tasks such as those for image, audio, and text classification. However, such neural networks are vulnerable to adversarial examples.
Hyun Kwon
doaj +1 more source
A Hybrid Adversarial Attack for Different Application Scenarios
Applied Sciences, 2020 Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with
Xiaohu Du +6 more
doaj +1 more source
Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
doaj +1 more source