Diverse Batch Steganography Using Model-Based Selection and Double-Layered Payload Assignment

Batch steganography regarding to image-selection and payload-allocation has gained increasing attention due to the secure demanding of data hiding of real scenario. However, due to the predefined selection mechanism, the chosen images are always complex which means that the diversity of the selected cover set is finite. In this paper, we develop a diverse and secure batch steganography scheme including the model-based generation and double-layered payload assignment. To construct the diverse image set, we use the Kullback-Leibler (KL) divergence to quantify the diversity increment and, relying on steganographic distortion, we select multiple image subsets (class) to create the diverse cover set in which each subset is modeled as the normal distribution with proper model parameters. Depending on the distortion of image subset, we assign the payload into all subsets with between-class allocation. Moreover, for the assigned payload of each subset, we introduce the linear model to achieve the within-class allocation. Finally, we obtain a diverse cover set along with suitable payload. Extensive experiments demonstrate the practicality of the proposed method in diversity and, compared with other selection methods, exhibit higher security on multiple steganalytic tools.