Results 11 to 20 of about 6,341 (246)
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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Adversarial Attack with Raindrops
CoRR, 2023 10 pages, 7 figures, This manuscript was submitted to CVPR ...
Jiyuan Liu 0005 +4 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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CoRR, 2022 ICML Workshop 2022 on Adversarial Machine Learning ...
Soichiro Kumano +2 more
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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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CoRR, 2022 Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Thomas Cilloni +2 more
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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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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model ...
Xiaofeng Mao +5 more
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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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CoRR, 2021 Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model ...
Andjela Mladenovic +6 more
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