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Super-Resolution Reconstruction of Reservoir Core CT Images via GAN: Advancing Energy Extraction Accuracy. [PDF]

Sensors (Basel)
Lv X, Li Y.
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Adversarial learning

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2].
Daniel Lowd, Christopher Meek
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Adversarial Training With Anti-Adversaries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adversarial training is effective in improving the robustness of deep neural networks. However, existing studies still exhibit significant drawbacks in terms of the robustness, generalization, and fairness of models. In this study, we validate the importance of different perturbation directions (i.e., adversarial and anti-adversarial) and bounds from ...
Xiaoling Zhou, Ou Wu 0001, Nan Yang
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AdvSGAN: Adversarial image Steganography with adversarial networks

Multimedia Tools and Applications, 2021
Steganalysers based on deep learning achieve state-of-the-art performance. However, due to the difficulty of capturing the distribution of the high-dimensional covers, traditional steganography schemes construct more complex artificial rules within expert knowledge, which is usually challenging to obtain to counter these powerful steganalysers ...
Lin Li 0082, Mingyu Fan, Defu Liu
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Adversary of the Queen’s Adversaries

1967
The war in the Netherlands was more effective in destroying the reputation of Leicester than that of the Spanish army. The Earl’s assumption in January 1586 of the governorship, which implied an English claim to sovereignty, enraged Elizabeth. Her object was to restore the liberties of the Netherlands but maintain the nominal suzerainty of Spain ...
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Adversarial and counter-adversarial support vector machines

Neurocomputing, 2019
Abstract A support vector machine (SVM) is a simple but yet powerful classification technique widely used in various applications, such as handwritten digits classification and face recognition. However, as any linear classification algorithm, it is vulnerable to adversarial attacks on test/training data.
Ihor Indyk, Michael Zabarankin
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Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2023
Ke He, Dong Seong Kim, Muhammad Rizwan Asghar   +2 more
exaly  

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Yulong Wang, Shenghong Li, Xin Yuan
exaly