Side Channel Steganalysis: When Behavior is Considered in Steganographer Detection

摘要

This paper intents to solve the challenging problem of steganographer detection in the real world from a new perspective: side channel attack. We propose utilizing the behavior of actors in the social network to identify the steganographer. While there are many behavior information may expose the steganographer, we just consider the correlation between images sequence as an example in this paper. Base on the assumption that the steganographer choosing images for communication randomly, we design the feature of subtractive images adjacent model (SIAM) to represent the correlations between images sequence of each actor. And then a binary classifier is used to identify the steganographer. To simulate the real world, the images in the experiment are all crawled from twitter. The experimental result shows good performance of our method.