Results 21 to 30 of about 1,209,773 (317)
Adversarial Attacks on Neural Networks for Graph Data [PDF]
Knowledge Discovery and Data Mining, 2018 Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.
Daniel Zügner +2 more
semanticscholar +1 more source
Adversarial Attacks on Time Series [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model
Fazle Karim +2 more
openaire +3 more sources
GANBA: Generative Adversarial Network for Biometric Anti-Spoofing
Applied Sciences, 2022 Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by spoofing attacks. To increase the robustness against spoofing attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting ...
Alejandro Gomez-Alanis +2 more
doaj +1 more source
Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness
IEEE Access, 2020 Deep Neural Networks (DNNs) classifiers performance degrades under adversarial attacks, such attacks are indistinguishably perturbed relative to the original data.
Benjamin Appiah +4 more
doaj +1 more source
Discriminator-free Generative Adversarial Attack [PDF]
Proceedings of the 29th ACM International Conference on Multimedia, 2021 9 pages, 6 figures, 4 ...
Lu, Shaohao +7 more
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Advances in adversarial attacks and defenses in computer vision: A survey [PDF]
IEEE Access, 2021 Deep Learning is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical ...
Naveed Akhtar +3 more
semanticscholar +1 more source
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization [PDF]
Computer Vision and Pattern Recognition, 2023 Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to
Jianping Zhang +3 more
semanticscholar +1 more source
Gradient-based Adversarial Attacks against Text Transformers [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 We propose the first general-purpose gradient-based adversarial attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix ...
Chuan Guo +3 more
semanticscholar +1 more source
Probabilistic Categorical Adversarial Attack & Adversarial Training
, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
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Textual Adversarial Training Method Based on Distributed Perturbation [PDF]
Jisuanji gongcheng, 2023 Text adversarial defense aims to enhance the resilience of neural network models against different adversarial attacks. The current text confrontation defense methods are usually only effective against certain specific confrontation attacks and have ...
Zhidong SHEN, Hengxian YUE
doaj +1 more source