AEAED: Attention-Enhanced Autoencoder for Adversarial Example Detection with Multi-Scale Feature Learning

Deep learning models face significant security threats from adversarial attacks, which can mislead model predictions by introducing subtle perturbations to input images. Existing adversarial example detection methods often suffer from limited detection accuracy and poor generalization capabilities. To address these challenges, we propose AEAED (Attention-Enhanced Autoencoder for Adversarial Example Detection with Multi-Scale Feature Learning), a novel detection model that integrates Transformer's multi-head self-attention mechanism into an autoencoder framework, significantly enhancing the model's ability to capture both global and local image features. AEAED comprises three key components: (1) a multi-scale attention encoder that combines convolutional layers' local feature extraction capabilities with self-attention mechanisms for global dependency modeling, improving sensitivity to adversarial perturbations; (2) an adaptive reconstruction decoder that leverages multi-head self-attention mechanisms to achieve high-quality image reconstruction; and (3) a comprehensive reconstruction error calculation method that integrates pixel-level errors and feature layer discrepancies, coupled with an adaptive threshold strategy for adversarial example detection. Experimental results on the CIFAR-10 dataset demonstrate that AEAED significantly outperforms existing baseline models, including MagNet, DAGMM, and SafetyNet, across multiple evaluation metrics. Notably, when detecting FGSM adversarial examples generated using the Swin Transformer, the model achieves an accuracy of 87.10%, a recall of 85.40%, and an AUC-ROC value of 0.89, while reducing the false positive rate to 0.08. These results conclusively validate the effectiveness and superiority of the proposed method in adversarial example detection tasks.