Results 11 to 20 of about 94,861 (274)
DroidEnemy: Battling adversarial example attacks for Android malware detection
Digital Communications and Networks, 2022 In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular,
Neha Bala +5 more
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Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
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WordRevert: Adversarial Examples Defence Method for Chinese Text Classification
IEEE Access, 2022 Adversarial examples can evade the detection of text classification models based on Deep Neural Networks (DNNs), thus posing a potential security threat to the system.
Enhui Xu +5 more
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Journal of Imaging, 2022 Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing.
Wei Zong +4 more
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A Cascade Defense Method for Multidomain Adversarial Attacks under Remote Sensing Detection
Remote Sensing, 2022 Deep neural networks have been widely used in detection tasks based on optical remote sensing images. However, in recent studies, deep neural networks have been shown to be vulnerable to adversarial examples.
Wei Xue +4 more
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Energy-Based Adversarial Example Detection for SAR Images
Remote Sensing, 2022 Adversarial examples (AEs) bring increasing concern on the security of deep-learning-based synthetic aperture radar (SAR) target recognition systems. SAR AEs with perturbation constrained to the vicinity of the target have been recently in the spotlight ...
Zhiwei Zhang +4 more
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Frontiers in Energy Research, 2023 The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless,
Donghe Li +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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Survey on adversarial attacks and defense of face forgery and detection
网络与信息安全学报, 2023 Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
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An interpretable approach for trustworthy intrusion detection systems against evasion samples
CTU Journal of Innovation and Sustainable Development, 2023 In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance,
Ngoc Tai Nguyen +3 more
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