A Robust Image Steganography Method Resistant to Scaling and Detection
Yue Zhang, Xiangyang Luo, Jinwei Wang, Chunfang Yang, Fenlin Liu · 網際網路技術學刊 · 2018
The current image steganography algorithms mainly focus on the anti-detectability rather than the robustness to scaling attacks. Therefore, it is difficult to extract the secret messages correctly after stego images subject to scaling attacks. To this end, an image steganography method for the nearest-neighbor interpolation method in image scaling is proposed, which can resist both scaling attack and statistical detection. First, the principle of nearest-neighbor interpolation method is analyzed and summarized, which is using the resized pixel coordinates to find the original adjacent pixels, and obtaining the weights of the adjacent original pixels by the distance between resized pixel and the original adjacent pixels, finally calculating the resized pixel value. Second, the scaling invariant pixels are extracted using the principle of nearest-neighbor interpolation method to generate a new cover image. Then the distortion functions in WOW, S-UNIWARD and MiPOD are used to calculate the new cover’s distortion to minimize the distortion embedding using STCs coding. Finally, resize the stego image to the original size. The steganalysis experiments based on BossBase-1.01 image library and SPAM, maxSRM features demonstrate that the proposed method has good resistance of scaling attack and Statistical detection under various scaling factors and payloads.