Results 61 to 70 of about 834,802 (152)
Universal attention guided adversarial defense using feature pyramid and non-local mechanisms
Scientific ReportsDeep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, significantly hindering the development of deep learning technologies in high-security domains. A key challenge is that current defense methods often lack universality,
Jiawei Zhao +6 more
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Adversarial Attack and Defense: A Survey
, 2022 In recent years, artificial intelligence technology represented by deep learning has achieved remarkable results in image recognition, semantic analysis, natural language processing and other fields.
Hongshuo Liang +4 more
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On Procedural Adversarial Noise Attack And Defense
CoRR, 2021 Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images. Researchers have been devoted to promoting the research on the universal adversarial perturbations (UAPs) which are gradient-free and have little prior knowledge on data ...
Jun Yan 0013 +3 more
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Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
Applied SciencesEscalating advancements in artificial intelligence (AI) has prompted significant security concerns, especially with its increasing commercialization. This necessitates research on safety measures to securely utilize AI models.
Inpyo Hong, Sokjoon Lee
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You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models
IEEE Access, 2021 Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang +3 more
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A divide-and-conquer reconstruction method for defending against adversarial example attacks
Visual IntelligenceIn recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However,
Xiyao Liu +5 more
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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
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Adversarial Attacks and Defense on Texts: A Survey
CoRR, 2020 Deep learning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis, and many others. However, in recent years it has been shown that these models possess weakness to noises which force the model to misclassify.
Aminul Huq, Mst. Tasnim Pervin
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Scientific ReportsMachine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity.
Zeinab Awad, Magdy Zakaria, Rasha Hassan
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On the Defense of Spoofing Countermeasures Against Adversarial Attacks
, 2023 Advances in speech synthesis have exposed the vulnerability of spoofing countermeasure (CM) systems. Adversarial attacks exacerbate this problem, mainly due to the reliance of most CM models on deep neural networks.
Thien-Phuc Doan +4 more
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