Integration Detection Model for Deep Neural Network Backdoor Attacks

Deep Neural Network DNN has demonstrated exceptional performance across various domains. However, with the continuous development of adversarial attack techniques, DNN faces increasingly serious security threats. Existing backdoor attack detection methods are primarily designed for specific attack scenarios and often exhibit insufficient effectiveness when confronting complex attack forms such as dynamic sensitivity optimization and randomized obfuscation. This study proposes an Integration Detection Model for Deep Neural Network Backdoor Attacks ID-Model , aiming to build an integrated detection framework capable of addressing various backdoor attacks. The ID-Model consists of three core components: the feature extraction and analysis module, the integrated detector module, and the data processing and alert module. Experimental results demonstrate that compared to STRIP and NNCDA methods, the ID-Model integrated detection model achieves a 19 improvement in detection accuracy under Original-Net and R-Net attacks. This research provides an important theoretical foundation for DNN security defense.