A Novel Embedding Distortion for Motion Vector-Based Steganography Considering Motion Characteristic, Local Optimality and Statistical Distribution

摘要

This paper presents an effective motion vector (MV)-based steganography to cope with different steganalytic models. The main principle is to define a distortion scale expressing the multi-level embedding impact of MV modification. Three factors including motion characteristic of video content, MV's local optimality and statistical distribution are considered in distortion definition. For every embedding location, the contributions of three factors are dynamically adjusted according to MV's property. Based on the defined distortion function, two layered syndrome-trellis codes (STCs) are utilized to minimize the overall embedding impact in practical embedding implementation. Experimental results demonstrate that the proposed method achieves higher level of security compared with other existing MV-based approaches, especially for high quality videos.