Video bitrate, as one of the important factors that reflect the video quality, can be easily manipulated via some video editing softwares. In some forensic scenarios, for example, video uploaders of video-sharing websites may increase video bitrate for seeking more commercial profits. In this paper, we try to detect those fake high bitrate videos, and then further to estimate their original bitrates. The proposed method is mainly based on the fact that if the video bitrate has been increased with the help of video editing software, its essential video quality will not increase at all. By analyzing the quality of the questionable video and a series of its re-encoded versions with different lower bitrates, we can obtain a feature curve to measure the change of the video quality, and then we propose a compact feature vector (3-D) to expose fake bitrate videos and their original bitrates. The experimental results evaluated on both CIF and QCIF raw sequences have shown the effectiveness of the proposed method.
The analysis of video compression history is one of the important issues in video forensics. It can assist forensics analysts in many ways, e.g., to determine whether a video is original or potentially tampered with, or to evaluate the real quality of a re-encoded video, etc. In the existing literature, however, there are very few works targeting videos in HEVC format (the most recent standard), especially for the issue of the detection of transcoded videos. In this paper, we propose a novel method based on the statistics of Prediction Units (PUs) to detect transcoded HEVC videos from AVC format. According to the analysis of the footprints of HEVC videos, the frequencies of PUs (whether in symmetric patterns or not) are distinguishable between original HEVC videos and transcoded ones. The reason is that previous AVC encoding disturbs the PU partition scheme of HEVC. Based on this observation, a 5D and a 25D feature set are extracted from I frames and P frames, respectively, and are combined to form the proposed 30D feature set, which is finally fed to an SVM classifier. To validate the proposed method, extensive experiments are conducted on a dataset consisting of CIF (352 × 288) and HD 720p videos with a diversity of bitrates and different encoding parameters. Experimental results show that the proposed method is very effective at detecting transcoded HEVC videos and outperforms the most recent work.
For the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering technique by developing the following approaches: 1)optimize the original feature set, 2)adopt an alternative FCM (AFCM) clustering method, and 3)determine adaptively the optimal clustering number for low embedding rates. These approaches are then integrated to bring about the proposed scheme, i.e., the improved MHRW (IMHRW). Extensive simulations show that the proposed scheme improves the performance of multi-histogram-based reversible watermarking, and it is comparable to or even better than the state of the arts. This thus demonstrates the feasibility and effectiveness of the proposed scheme.
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