RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning [PDF]
Future Internet, 2019 With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a
Yong Fang +3 more
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MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack [PDF]
, 2021 Xu M, Zhang T, Zhang D.
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Deep learning models for electrocardiograms are susceptible to adversarial attack [PDF]
Nature Medicine, 2020 Xintian Han, Yuxuan Hu, Larry A Chinitz
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Multi-target Category Adversarial Example Generating Algorithm Based on GAN [PDF]
Jisuanji kexue, 2022 Although deep neural networks perform well in many areas,research shows that deep neural networks are vulnerable to attacks from adversarial examples.There are many algorithms for attacking neural networks,but the attack speed of most attack algorithms ...
LI Jian, GUO Yan-ming, YU Tian-yuan, WU Yu-lun, WANG Xiang-han, LAO Song-yang
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EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
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Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems
Frontiers in Big Data, 2022 Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems.
Siyu Wang +5 more
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Object Detection Adversarial Attack for Infrared Imagery in Remote Sensing [PDF]
Hangkong bingqi, 2022 Aiming at the problems of poor effect of existing adversarial attack for object detection algorithms on small-scale target attack, a large number of meaningless disturbances in adversarial samples and low disturbance genera-tion efficiency, taking ...
Qi Jiahao, Zhang Yu, Wan Pengcheng, Li Yuanzhe, Liu Xingyue, Yao Aihuan, Zhong Ping
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A Multimodal Adversarial Attack Framework Based on Local and Random Search Algorithms
International Journal of Computational Intelligence Systems, 2021 Although many problems in computer vision and natural language processing have made breakthrough progress with neural networks, adversarial attack is a serious potential problem in many neural network- based applications.
Zibo Yi, Jie Yu, Yusong Tan, Qingbo Wu
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Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection
Remote Sensing, 2021 With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote
Mingming Lu, Qi Li, Li Chen, Haifeng Li
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Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images
Remote Sensing, 2022 Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack.
Yichuang Zhang +6 more
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