Results 61 to 70 of about 852,288 (193)
DAD++: Improved Data-free Test Time Adversarial Defense [PDF]
, 2023 With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real ...
Chakraborty, Anirban +4 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
doaj +1 more source
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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Adversarial Defense by Latent Style Transformations
CoRR, 2020 Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs.
Shuo Wang 0012 +5 more
openaire +3 more sources
Efficient Defenses Against Adversarial Attacks [PDF]
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017 Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the
Valentina Zantedeschi +2 more
openaire +4 more sources
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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Adversarial defenses via vector quantization
NeurocomputingThis is the author-accepted version of our paper published in Neurocomputing.
Zhiyi Dong, Yongyi Mao
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Towards adversarial defense with multi-stage information fusion for visual tracking
Engineering Science and Technology, an International JournalIn recent years, the effectiveness and stealth of adversarial attack methods have been continuously improving, posing significant challenges to the robustness and accuracy of visual tracking.
Peng Gao +4 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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