Results 41 to 50 of about 29,955 (264)
Boundary Adversarial Examples Against Adversarial Overfitting
CoRR, 2022 Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing ...
Muhammad Zaid Hameed, Beat Buesser
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Survey of Image Adversarial Example Defense Techniques [PDF]
Jisuanji kexue yu tansuo, 2023 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots.
LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu
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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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Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
IEEE Access, 2018 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon +4 more
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Adversarial Examples in the Physical World [PDF]
, 2018 Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it.
Alexey Kurakin +2 more
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Optimized Adversarial Example With Classification Score Pattern Vulnerability Removed
IEEE Access, 2022 Neural networks provide excellent service on recognition tasks such as image recognition and speech recognition as well as for pattern analysis and other tasks in fields related to artificial intelligence.
Hyun Kwon, Kyoungmin Ko, Sunghwan Kim
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POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI
IEEE AccessAdversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee +3 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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Detecting Adversarial Examples
CoRRDeep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original ...
Furkan Mumcu, Yasin Yilmaz 0001
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Adversarial Attacks to Manipulate Target Localization of Object Detector
IEEE AccessAdversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu +7 more
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