Enhancing DNN Adversarial Robustness via Dual Stochasticity and Geometric Normalization. [PDF]
Sensors (Basel)Wu X, Han G.
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Evaluating gait system vulnerabilities through PPO and GAN-generated adversarial attacks. [PDF]
Sci RepSaoudi EM, Jaafari J, Jai Andaloussi S.
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How online studies must increase their defences against AI. [PDF]
Commun PsycholAnders G, Buder J, Papenmeier F, Huff M.
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PromptGuard a structured framework for injection resilient language models. [PDF]
Sci RepAlzahrani A.
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Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems. [PDF]
Sci RepRekik S, Mehmood S.
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FortiNIDS: Defending Smart City IoT Infrastructures Against Transferable Adversarial Poisoning in Machine Learning-Based Intrusion Detection Systems. [PDF]
Sensors (Basel)Alajaji A.
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Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances. [PDF]
Sensors (Basel)Alsabaan M +6 more
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Exploring adversarial attacks and defenses
Deep Learning classifiers are capable of an outstanding performance. Yet, they are vulnera ble to adversarial attacks, i.e. it is possible to craft a slightly modified version of a correctly classified image that, although its contents are still clearly recognisable to a human being, the classifier outputs an incorrect classification. In this thesis we
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Energy-Efficient and Adversarially Resilient Underwater Object Detection via Adaptive Vision Transformers. [PDF]
Sensors (Basel)Li L, Zhang G, Zhou Y.
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Optimized CatBoost machine learning (OCML) for DDoS detection in cloud virtual machines with time-series and adversarial robustness. [PDF]
Sci RepSamy H, Bahaa-Eldin AM, Sobh MA, Taha A.
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