Multiclassification Tampering Detection Algorithm Based on Spatial-Frequency Fusion and Swin-T

Abstract

Deep learning methods for image forgery detection often struggle with compression attack robustness. This paper proposes a novel multi-class forgery detection framework combining spatial-frequency fusion with Swin-Transformer, outperforming existing methods in compression attack scenarios. Our approach integrates a frequency domain perception module with quantization tables, a spatial domain perception module through multi-strategy convolutions, and a dual-attention mechanism combining spatial and channel attention for feature fusion. Experimental results demonstrate superior performance with an F1 score of 87% under JPEG compression (q = 75), significantly surpassing current state-of-the-art methods by an average of 15% in compression resistance while maintaining high detection accuracy.