Abstract
In order to defraud click-through rate, some merchants recompress the low-bitrate video to a high-bitrate one without improving the video quality. This behavior deceives viewers and wastes network resources. Therefore, a stable algorithm that detects fake bitrate videos is urgently needed. High-Efficiency Video Coding (HEVC) is a worldwide popular video coding standard. Hence, in this paper, a robust algorithm is proposed to detect HEVC fake bitrate videos. Firstly, five effective feature sets are extracted from the prediction process of HEVC, including Coding Unit of I-picture/P-picture partitioning modes, Prediction Unit of I-picture/P-picture partitioning modes, Intra Prediction Modes of I-picture. Secondly, feature concatenation is adopted to enhance the expressiveness and improve the effectiveness of the features. Finally, five single feature sets and three concatenate feature sets are separately sent to the support vector machine for modeling and testing. The performance of the proposed algorithm is compared with state-of-the-art algorithms on HEVC videos of various resolutions and fake bitrates. The results show that the proposed algorithm can not only can better detect HEVC fake bitrate videos, but also has strong robustness against frame deletion, copy-paste, and shifted Group of Picture structure attacks.
Highlights
Digital video has become an indispensable part of our daily lives nowadays
We present an algorithm to detect recompressed High-Efficiency Video Coding (HEVC) videos with fake bitrate by classification features extracted from prediction process
Reconstruction error is caused by reference frame in the reconstruction process. It means that the difference between P-PU partitioning sequence in HEVC double-compressed video with fake bitrate and single-compressed video is mainly caused by quantization error and reconstruction error
Summary
Digital video has become an indispensable part of our daily lives nowadays. According to the statistics, about 65,000 videos are uploaded to YOUTUBE every day [1]. We presented an algorithm for detecting recompressed HEVC videos with fake bitrates [18]. We present an algorithm to detect recompressed HEVC videos with fake bitrate by classification features extracted from prediction process. Three concatenation features are separately sent to SVM to distinguish recompressed HEVC videos with fake bitrate from single-compressed. We propose an efficient method to identify recompressed HEVC videos with fake bitrate, and its classification accuracy is much higher than the previous algorithms. It is the first time, to the best of our knowledge, to comprehensively extract multiple effective prediction features from HEVC prediction process.
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