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Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems.
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A. Sotgiu, A. Demontis, M. Melis, B. Biggio, G. Fumera, X. Feng, and F. Roli, “Deep neural rejection against adversarial examples,” EURASIP Journal on Information Security , vol. 2020, pp. 1–10, 2020
2020
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D. J. Miller, Z. Xiang, and G. Kesidis, “Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks,” Proceedings of the IEEE , vol. 108, no. 3, pp. 402–433, 2020
2020
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2020
Closest in time.
F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” in International Conference on Machine Learning , 2020, pp. 1–12
2020
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G. Ciravegna, F. Giannini, M. Gori, M. Maggini, and S. Melacci, “Human-driven fol explanations of deep learning,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , C. Bessiere, Ed. International Joint Conferences on Artificial Intelligence Organization, 7 2020, pp. 2234–2240, main track
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A. Sotgiu, A. Demontis, M. Melis, B. Biggio, G. Fumera, X. Feng, and F. Roli, “Deep neural rejection against adversarial examples,” EURASIP J. Information Security , vol. 2020, no. 5, 2020
2020
Closest in time.
F. Croce and M. Hein, “Minimally distorted adversarial examples with a fast adaptive boundary attack,” in Proceedings of the 37th International Conference on Machine Learning , vol. 119. PMLR, 13–18 Jul 2020, pp. 2196–2205
2020
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2021
Closest in time.