Results 281 to 290 of about 5,389,393 (319)
Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification. [PDF]
Brain InformZhang J, Zhang H, Yang Y.
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Selective Audio Adversarial Example in Evasion Attack on Speech Recognition System
IEEE Transactions on Information Forensics and Security, 2020 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, and other pattern analysis tasks. Despite the success of DNNs, these systems can be exploited by what is termed adversarial examples.
Hyun Kwon, Yongchul Kim, Hyunsoo Yoon
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Adversarial-Example Attacks Toward Android Malware Detection System
IEEE Systems Journal, 2020Recently, it was shown that the generative adversarial network (GAN) based adversarial-example attacks could thoroughly defeat the existing Android malware detection systems.
Heng Li, Wei Yuan, Jiahuan Li
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AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
IEEE International Conference on Computer Vision, 2023Previous work on adversarial examples typically involves a fixed norm perturbation budget, which fails to capture the way humans perceive perturbations.
Xinquan Chen +4 more
semanticscholar +1 more source
MANDA: On Adversarial Example Detection for Network Intrusion Detection System
IEEE Conference on Computer Communications, 2021With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks.
Ning Wang +4 more
semanticscholar +1 more source
Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems
IEEE Transactions on Cybernetics, 2021Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs).
Jiwei Tian +5 more
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Adversarial Example Detection Using Latent Neighborhood Graph
IEEE International Conference on Computer Vision, 2021Detection of adversarial examples with high accuracy is critical for the security of deployed deep neural network-based models. We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an ...
Ahmed A. Abusnaina +6 more
semanticscholar +1 more source
Adversarial example detection for DNN models: a review and experimental comparison
Artificial Intelligence Review, 2021Deep learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance systems, autonomous vehicles and healthcare. Such safety-critical applications
Ahmed Aldahdooh +3 more
semanticscholar +1 more source
ACM Transactions on Multimedia Computing, Communications and ApplicationsAdversarial example detection, which can be conveniently applied in many scenarios, is important in the area of adversarial defense. Unfortunately, existing detection methods suffer from poor generalization performance, because their training process ...
Yunhong Wang, Ruijie Yang, Beichen Li
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