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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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Tactical-Grade Wearables and Authentication Biometrics. [PDF]
Sensors (Basel)Agiomavritis F, Karanasiou I.
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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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Adversarial Attacks and Defenses on Graphs
ACM SIGKDD Explorations Newsletter, 2021Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009 +6 more
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Sinkhorn Adversarial Attack and Defense
IEEE Transactions on Image Processing, 2022Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
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Adversarial Attacks and Defenses
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002 +3 more
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Multi-view Defense with Adversarial Autoencoders
2021 International Joint Conference on Neural Networks (IJCNN), 2021In view of the vulnerability of multi-view deep models to adversarial perturbation, this paper designs two kinds of multi-view adversarial autoencoders (MAAEs). We first propose the MAAE1 defense, in which each view is used to train the corresponding single-view adversarial autoencoders separately, and then the reconstructed output is fed into the ...
Xuli Sun, Shiliang Sun
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DeepRobust: a Platform for Adversarial Attacks and Defenses
Proceedings of the AAAI Conference on Artificial Intelligence, 2021DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001 +3 more
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