Diffusion-generated face image identification technique based on diffusion reconstruction error feature

Traditional deep learning detectors often struggle to generalise when detecting diffusion-generated content. To address this, we propose DIRE, a generalised detector leveraging reconstruction error image representation. The framework standardises facial feature spaces through constrained feature learning and introduces a gradient suppression algorithm to filter abnormal gradients, preventing shortcut learning and enhancing generalisable feature extraction. Experiments on hybrid datasets validate DIRE's effectiveness in four cross-domain tasks (O&C&M→I, O&C&I→M, O&I&M→C, and I&C&M→O). Ablation studies confirm the synergy of feature standardisation and gradient suppression, reducing bias by 97.6% and parameters by 42%, while accelerating inference by 2.3×. DIRE achieves 98.2% and 96.7% accuracy on two tasks (O&C&I→M and O&M&I→C), outperforming state-of-the-art methods by 5.3% while maintaining computational efficiency. This study advances generative face detection through dual optimisation, offering a lightweight framework for financial identity verification and social media content moderation.