Batch Steganography via Generative Network
Nan Zhong, Zhenxing Qian, Zichi Wang, Xinpeng Zhang, Xiaolong Li
Abstract
Batch steganography is a technique that hides information into multiple covers. To achieve a better performance on the security of data hiding, we propose a novel strategy of batch steganography using a generative network. In this method, the approaches of cover selection, payload allocation, and distortion evaluation are considered in the round. We define a quality metric to evaluate the distortion between the cover image and the stego. When training the generation function, we define an objective function containing two parts: the entropy loss and the steganalytic loss. While the entropy loss is used to represent the gap between the payload inside stego images and the entire embedding capacity, the steganalytic loss is used to assess the data embedding impact using the proposed quality metric. With back-propagation, we minimize the objective function to obtain an optimal solution. Accordingly, different payloads can be allocated to different images, and the ± 1 modification probability for pixels in each cover can be calculated. Finally, we embed information into the selected images by STC. Experimental results show that the proposed method achieves a better undetectability against modern steganalytic tools.