Lightweight Scale-Free Steganalysis Mining Dispersed Clues in Downward JPEG-Resistant Steganography

With the widespread adoption of smart devices and social networking platforms, the development of robust image steganography techniques for public lossy channels has become increasingly crucial. Among JPEG-resistant steganographic methods, PMAS (Postprocessing and precise dither Modulation based robust Adaptive Steganography) has demonstrated superior performance by utilizing high-quality images and maintaining resilience against aggressive compression. This method achieves remarkable concealment in user-shared images, presenting substantial challenges to public communication security. To counter this threat, we propose a specialized lightweight Scale-Free Network for mining Clues in downward JPEG-resistant steganography (SF-ClueNet), specifically designed to identify vulnerabilities in PMAS despite its sophisticated anti-detection mechanisms. Departing from conventional approaches that depend on high-pass filter residuals, SF-ClueNet extracts comprehensive global statistical features, enabling effective detection of dispersed steganographic artifacts. When integrated with lightweight residual feature miner, our method maintains pattern recognition capabilities as image dimensions increase, ensuring consistent detection performance. Experimental results demonstrate that SF-ClueNet significantly enhances detection accuracy, exhibits robust performance against data distribution shifts with minimal transfer loss, and supports direct analysis of high-resolution images. These advanced capabilities position SF-ClueNet as a viable and efficient solution for practical steganalysis applications across diverse operational environments.