Two Embedding Strategies for Payload Distribution in Multiple Images Steganography

Abstract

With the coming of the era of big data, digital images are growing explosively on the Internet. Traditional steganography which embeds payload into one cover image is facing new challenges. In fact, a steganographer could utilize multiple images to embed payload simultaneously. How to fully exploit image features to allocate payload for performance enhancement is still an open issue. In this paper, we first establish a framework to formulate data embedding in multiple images. Two new embedding strategies based on image texture complexity and distortion distribution are designed for payload distribution, which can be implemented together with these state-of-art single image steganographic algorithms. Experimental results demonstrate that two proposed embedding strategies in multiple images steganography could obtain better statistical undetectability.