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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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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
doaj +1 more source
Attention, Please! Adversarial Defense via Activation Rectification and Preservation [PDF]
, 2022 This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.
Yu, Jian +4 more
core +1 more source
Survey on adversarial attacks and defenses for object detection
Tongxin xuebao, 2023 In response to recent developments in adversarial attacks and defenses for object detection, relevant terms and concepts associated with object detection and adversarial learning were first introduced.Subsequently, according to the evolution process of ...
Xinxin WANG +6 more
doaj +2 more sources
Adversarial Recommender Systems: Attack, Defense, and Advances
, 2022 Adversarial machine learning is the research field investigating vulnerabilities inherent to machine learning systems’ design and ways to defend against them.
Di Noia, Tommaso +3 more
core +1 more source
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
CoRR, 2017 Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently
Warren He +4 more
openaire +4 more sources
Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks [PDF]
, 2020 Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems.
Xiang Li 0080, Shihao Ji 0001
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Complex & Intelligent SystemsNeural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model
Xinlei Liu +6 more
doaj +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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Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
CoRR, 2017 We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.
Hyeungill Lee +2 more
openaire +2 more sources