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Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant focus of defense. In this work, we propose a deep learning defense known as multi-source adversarial perturbations elimination (MAPE) to counter diverse transferable attacks. MAPE comprises the single-source adversarial perturbation elimination (SAPE) mechanism and the pre-trained models probabilistic scheduling algorithm (PPSA). SAPE utilizes a thoughtfully designed channel-attention U-Net as the defense model and employs adversarial examples generated by a pre-trained model (e.g., ResNet) for its training, thereby enabling the elimination of known adversarial perturbations. PPSA introduces model difference quantification and negative momentum to strategically schedule multiple pre-trained models, thereby maximizing the differences among adversarial examples during the defense model’s training and enhancing its robustness in eliminating adversarial perturbations. MAPE effectively eliminates adversarial perturbations in various adversarial examples, providing a robust defense against attacks from different substitute models. In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1% on CIFAR-10 and over 71.5% on Mini-ImageNet, demonstrating state-of-the-art performance. [ABSTRACT FROM AUTHOR]

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Implementing defensive deception in the cloud is promising to proactively counter reconnaissance attack. This technique presents decoys to camouflage cloud assets and distracts attack resource. However, the major challenge is to develop an effective deception strategy to orchestrate digital decoys. To address this issue, we propose a deep reinforcement learning (DRL)-based defensive deception framework. First, we formulate a utility function, which mathematically models underlying threats associated with common vulnerabilities among virtual machines in the cloud. Then, we customize training interfaces and the neural networks for a DRL agent. The reward function reflects the effectiveness of asset concealment and the waste of attack resources, referring to a comprehensive defense goal. Finally, the well-trained DRL agent generates the optimal defense strategy. It specifies a more granular deception strategy than existing proposals. Simulation results show that the proposed framework leads to a 7.87% average advantage in realizing the comprehensive defense goal. Moreover, it can stably improve the concealment degree of cloud assets up to 20.58%, and increase the attack cost up to 40.40%. This study shows that it is promising to improve cloud security with deception defense and artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

A new terbium 2-isopropylimidazole-4,5-dicarboxylic acid complex was prepared and incorporated into titanium dioxide matrix by mild sol–gel method. Then we fabricated a terbium luminescent hybrid material, which displayed striking green emission even in pure water. It was interesting to find that this target material exhibited highly selective and fast (1 s) quenching effect to F− compared with CH3COO−, Cl−, Br−, I−. We recognized that the hydrogen bonding interactions between fluoride and ligand resulted in the recognition process. More significantly, this hybrid titania material prepared under low temperature (80 °C) could be used in photodegradation of methyl orange in aqueous environment. [ABSTRACT FROM AUTHOR]