Results 41 to 50 of about 82,924 (315)
Text Adversarial Purification as Defense against Adversarial Attacks
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023 Accepted by ACL2023 main ...
Linyang Li, Demin Song, Xipeng Qiu
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Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
, 2018 Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification.
Dong, Yinpeng +5 more
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Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
CoRR, 2020 Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Adversarial attacks are often generated by maximizing standard losses such as the cross-entropy loss or maximum ...
Gaurang Sriramanan +3 more
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An Empirical Review of Adversarial Defenses
CoRR, 2020 19 pages, 8 Figures, Report Reviewed by Vivek ...
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Towards Adversarial Robustness for Multi-Mode Data through Metric Learning
Sensors, 2023 Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...
Sarwar Khan +3 more
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Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
, 2018 Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels.
Chin, Peter +6 more
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Study of Pre-processing Defenses against Adversarial Attacks on\n State-of-the-art Speaker Recognition Systems [PDF]
, 2021 Sonal Joshi +4 more
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, 2019 Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs).
Ahmed, Rehan +6 more
core +1 more source
Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
Applied Sciences, 2021 Voice Processing Systems (VPSes), now widely deployed, have become deeply involved in people’s daily lives, helping drive the car, unlock the smartphone, make online purchases, etc.
Xiaojiao Chen, Sheng Li, Hao Huang
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Defensive Dual Masking for Robust Adversarial Defense
Computational LinguisticsAbstract Adversarial defenses for textual data have gained considerable attention in recent years due to the increasing vulnerability of Natural Language Processing (NLP) models to adversarial attacks. These attacks exploit subtle perturbations in input text to deceive models, posing significant challenges to model robustness and ...
Wangli Yang +3 more
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