BeniFul: Backdoor Defense via Middle Feature Analysis for Deep Neural Networks
This addresses security vulnerabilities in deep neural networks for applications like image classification, though it is incremental as it builds on existing defense methods.
The paper tackles the problem of backdoor attacks in deep neural networks by proposing BeniFul, a defense method that uses middle feature analysis to detect backdoor inputs and eliminate backdoors from models, achieving strong performance on CIFAR-10 and Tiny ImageNet against five state-of-the-art attacks.
Backdoor defenses have recently become important in resisting backdoor attacks in deep neural networks (DNNs), where attackers implant backdoors into the DNN model by injecting backdoor samples into the training dataset. Although there are many defense methods to achieve backdoor detection for DNN inputs and backdoor elimination for DNN models, they still have not presented a clear explanation of the relationship between these two missions. In this paper, we use the features from the middle layer of the DNN model to analyze the difference between backdoor and benign samples and propose Backdoor Consistency, which indicates that at least one backdoor exists in the DNN model if the backdoor trigger is detected exactly on input. By analyzing the middle features, we design an effective and comprehensive backdoor defense method named BeniFul, which consists of two parts: a gray-box backdoor input detection and a white-box backdoor elimination. Specifically, we use the reconstruction distance from the Variational Auto-Encoder and model inference results to implement backdoor input detection and a feature distance loss to achieve backdoor elimination. Experimental results on CIFAR-10 and Tiny ImageNet against five state-of-the-art attacks demonstrate that our BeniFul exhibits a great defense capability in backdoor input detection and backdoor elimination.