SF-CAMNet: A spatial–frequency fusion network for robust spatial-domain image steganalysis

Abstract

Spatial domain image steganalysis has become increasingly important due to the widespread use of digital images for communication and data sharing over the internet and social media. While steganography enables secure communication and digital watermarking, it can also be exploited for malicious purposes, making reliable detection essential. This study proposes SF-CAMNet, a deep learning framework for spatial-domain image steganalysis that integrates spatial and frequency-domain features. The model combines Spatial Rich Model (SRM) filters, the Convolutional Block Attention Module (CBAM), Mish activation, and a Discrete Wavelet Transform (DWT) branch to enhance the detection of subtle embedding artifacts. Performance was evaluated on the BOSSBase v1.01 dataset using S-UNIWARD, WOW, HILL, HUGO, and MiPOD at payloads of 0.2 and 0.4 bpp. Cross-dataset evaluation on the BOWS2 dataset further demonstrates the robustness and generalization capability of the proposed model. Experimental results show that SF-CAMNet achieves competitive performance compared with state-of-the-art spatial-domain image steganalysis methods.