Double compression detection based on local motion vector field analysis in static-background videos

• A novel double compression detection method in static-background videos is proposed.
• Local motion vector field analysis is used to obtain segmentation of video contents.
• Modification based on local motion strengths is used to extract robust fingerprints.
• The proposed method has more robust performance than several state-of-the-art methods.

Videos captured by stationary cameras are widely used in video surveillance and video conference. This kind of video often has static or gradually changed background. By analyzing the properties of static-background videos, this work presents a novel approach to detect double MPEG-4 compression based on local motion vector field analysis in static-background videos. For a given suspicious video, the local motion vector field is used to segment background regions in each frame. According to the segmentation of backgrounds and the motion strength of foregrounds, the modified prediction residual sequence is calculated, which retains robust fingerprints of double compression. After post-processing, the detection and GOP estimation results are obtained by applying the temporal periodic analysis method to the final feature sequence. Experimental results have demonstrated better robustness and efficiency of the proposed method in comparison to several state-of-the-art methods. Besides, the proposed method is more robust to various rate control modes.