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Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks
In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a significant vulnerability: the potential for backdoors that can be exploited by malicious actors.
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained model and fine tune it.
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks
The increasing importance of both deep neural networks (DNNs) and cloud services for training them means that bad actors have more incentive and opportunity to insert backdoors to alter the behavior of trained models.
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