Upward Robust Steganography Based on Overflow Alleviation
Kai Zeng, Kejiang Chen, Weiming Zhang, Yaofei Wang
Abstract
Images with low quality factor (QF) are widely available and apposite as steganography cover, which will be JPEG recompressed with a preset larger QF when uploaded to online social networks. This scenario is known as “Upward Robust,” which is currently a hotspot of robust steganography. The state-of-the-art algorithm is Generalized dither Modulation-based robust Adaptive Steganography (GMAS). However, GMAS can only realize limited resistance to detection and compression due to robust domain selection. To overcome this problem, we meticulously explore three lossy operations in JPEG recompression and discover that the key problem is spatial overflow. Then, two preprocessing methods, overall scaling (OS) and specific truncation (ST), were presented to remove overflow before message embedding and generate a reference image. After pre-processing, the stability of the image coefficients during JPEG recompression will be significantly enhanced. Therefore, we no longer need robust domain selection and all coefficients are eligible as cover, which improves security and embedding capacity. Additionally, the reference image was employed as guidance to build asymmetric distortion for removing overflow during embedding. Experimental results show that the proposed methods significantly surpass GMAS in terms of security and achieve comparable robustness.