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Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems. [PDF]

Sci Rep
Rekik S, Mehmood S.
europepmc   +1 more source

Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning [PDF]

International Journal of Computer Vision, 2022
Accepted by International Journal of Computer Vision (IJCV) 2022. Code will be available at https://github.com/rshaojimmy/ECCV2020-OSAD.
Vishal M Patel, Pong Chi Yuen, Rui Shao
exaly   +3 more sources

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Adversarial Attacks and Defenses on Graphs

ACM SIGKDD Explorations Newsletter, 2021
Deep 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, Yaxin Li 0001, Han Xu 0002, Yiqi Wang 0001, Shuiwang Ji, Charu Aggarwal 0001, Jiliang Tang   +6 more
openaire   +1 more source

Sinkhorn Adversarial Attack and Defense

IEEE Transactions on Image Processing, 2022
Adversarial 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.
openaire   +2 more sources

Adversarial Attacks and Defenses

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Deep 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, Yaxin Li 0001, Wei Jin 0009, Jiliang Tang   +3 more
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DeepRobust: a Platform for Adversarial Attacks and Defenses

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
DeepRobust 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, Wei Jin 0009, Han Xu 0002, Jiliang Tang   +3 more
openaire   +1 more source

Adversarial Example Defense Based on the Supervision

2021 International Joint Conference on Neural Networks (IJCNN), 2021
In recent years, deep learning has developed rapidly and has shown great performance on many challenging machine learning tasks, such as image classification, natural language processing, and speech recognition. However, researchers have recently discovered that deep learning models have security risks and are easily affected by adversarial examples ...
Ziyu Yao 0003, Jiaquan Gao
openaire   +1 more source

MTD-AD: Moving Target Defense as Adversarial Defense

IEEE Transactions on Dependable and Secure Computing
Network Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He, Dan Dongseong Kim, Muhammad Rizwan Asghar   +2 more
openaire   +4 more sources