Robust Batch Steganography in Social Networks With Non-Uniform Payload and Data Decomposition

Abstract

Batch steganography refers to a steganography method where a user tries to hide confidential payload within a batch of images from social networks. It is significantly different from the traditional laboratory steganography where a user only considers an individual image. To apply batch steganography in social media networks, we are faced with two nontrivial problems: 1) how to assign payload to multiple images? and 2) how to recover the hidden payload if some images are lost during transmission? We tackle the problems by: 1) developing an optimal payload embedding strategy and 2) designing a special type of data decomposition. In the former, an optimal non-uniform payload distribution for multiple images is obtained by iterative feature back replacement. In the later, we employ special matrix operation to expand original data and split them into multiple shares. These shares are then embedded into different covers following the optimal non-uniform payload distribution. Our solution is robust in the sense that the recipient can recover the hidden data even if some images are intercepted or lost during delivery. Comprehensive experimental results show that our method outperforms the state-of-the-art in terms of anti-detectability and robustness.