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 F 1 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.