Exposing Fake Bit Rate Videos and Estimating Original Bit Rates

Abstract

Bit rate is one of the important criterions for digital video quality. With some video tools, however, video bit rate can be easily increased without improving the video quality at all. In such a case, a claimed high bit rate video would actually have poor visual quality if it is up-converted from an original lower bit rate version. Therefore, exposing fake bit rate videos becomes an important issue for digital video forensics. To the best of our knowledge, although some methods have been proposed for exposing fake bit rate MPEG-2 videos, no relative work has been reported to further estimate their original bit rates. In this paper, we first analyze the statistical artifacts of these fake bit rate videos, including the requantization artifacts based on the first-digit law in the DCT frequency domain (12-D) and the changes of the structural similarity indexes between the query video and its sequential bit rate down-converted versions in the spatial domain (4-D), and then we propose a compact yet very effective 16-D feature vector for exposing fake bit rate videos and further estimating their original bit rates. The extensive experiments evaluated on hundreds of video sequences with four different resolutions and two typical compression schemes (i.e., MPEG-2 and H.264/AVC) have shown the effectiveness of the proposed method compared with the existing relative ones.Index Terms-Digital video forensics, video bit rate estimation, video quality forgery.

Exposing Video Compression History by Detecting Transcoded HEVC Videos from AVC Coding

“…Besides, it might be employed to detect video splicing since different video clips might have different encoding history. Moreover, the analysis of the processing history of the video compression could be introduced in video quality assessment because the encoding information obtained from a video bitstream might not represent the actual visual quality [4], e.g., a "high"-quality HEVC video re-encoded from an originally low-quality AVC content.…”

Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion

“…The abundant temporal redundancy in digital video is easily used by fakers, leaving digital video open to the threat of tampering. Video frame rate up-conversion (FRUC) [1] is one of the commonest operations for tampering with digital video in the temporal domain, e.g., in order to obtain a high-definition label, some forgers often increase the bits of video by FRUC operation before uploading a video to a video-sharing website, which damages the economic interests of webmasters and audiences [2]. To guarantee the authenticity of video data, some digital forensic techniques are needed to detect the FRUC forgery.…”