Robust steganography with boundary-preserving overflow alleviation and adaptive error correction

With the rapid evolution of the Internet, the vast amount of data has created opportunities for fostering the development of steganographic techniques. However, traditional steganography faces challenges in social networks due to lossy operations such as spatial truncation during JPEG recompression, with limited research on their effects. Existing methods aim to ensure the stability of the quantized coefficients by reducing the effects of spatial truncation. Nevertheless, these approaches may induce notable alterations to image pixels, potentially compromising anti-steganalysis performance. In this study, we analyze the overflow characteristics of spatial blocks and observe that pixel values at the boundaries of spatial blocks are more prone to overflow. Based on this observation, we propose a preprocessing method that performs overflow removal operations according to the actual overflow conditions of spatial blocks, enhancing coefficient stability while minimizing modifications to spatial block boundaries, thereby ensuring image quality. Subsequently, we employ adaptive error correction coding to reduce coding redundancy, thereby augmenting robustness and mitigating its impact on anti-steganalysis performance. The experimental results indicate that the proposed method possesses a strong embedding capacity, maintaining a high level of robustness while enhancing security.