Detection of fake high definition for HEVC videos based on prediction mode feature

Yu, Yang; Yao, Haichao; Ni, Rongrong; Zhao, Yao

doi:10.1016/j.sigpro.2019.107269

Cited by 15 publications

(3 citation statements)

References 15 publications

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“…We consider two types of recompression processes, including recompression with the increased bit rate and recompression with the decreased bit rate. For the first case, double compressed videos are also called as fake bit rate videos in previous works [23,24], where the video quality of fake bit rate videos cannot be improved compared with their original videos. On the other hand, for the second case, forensic traces of recompression, such as relocated I-frames [10,17], suffer from a more distinct degradation caused by the severe lossy quantization.…”

Section: Statistical Analysis Of Cu Information With Differentmentioning

confidence: 99%

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Wang

Zhang

et al. 2021

Security and Communication Networks

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Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).

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Section: Statistical Analysis Of Cu Information With Differentmentioning

confidence: 99%

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Wang

Zhang

et al. 2021

Security and Communication Networks

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show abstract

“…They found that spatiotemporal patterns in biological signals can be conceived as a representative projection of residuals. Yu et al 8 proposed a novel method to detect the fake high definition for high efficient video coding videos based on prediction mode feature. Choi et al 5 proposed a novel fake video detection method by adopting predictive uncertainty in detection.…”

Section: Related Workmentioning

confidence: 99%

Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

Jia

et al. 2021

Sci Rep

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With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.

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“…Moreover, Yu et al (2020) [84] proposed a technique for distinguishing double HEVC compression by mixing the two approaches proposed in [7,44]. However, this work detects only whether the HEVC videos were double-compressed by a third party or not, even if it is re-compressed by the same bitrate.…”

Section: ) Authentication Challengesmentioning

confidence: 99%

HEVC Watermarking Techniques for Authentication and Copyright Applications: Challenges and Opportunities

et al. 2020

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Recently, High-Efficiency Video Coding (HEVC/H.265) has been chosen to replace previous video coding standards, such as H.263 and H.264. Despite the efficiency of HEVC, it still lacks reliable and practical functionalities to support authentication and copyright applications. In order to provide this support, several watermarking techniques have been proposed by many researchers during the last few years. However, those techniques are still suffering from many issues that need to be considered for future designs. In this paper, a Systematic Literature Review (SLR) is introduced to identify HEVC challenges and potential research directions for interested researchers and developers. The time scope of this SLR covers all research articles published during the last six years starting from January 2014 up to the end of April 2020. Forty-two articles have met the criteria of selection out of 343 articles published in this area during the mentioned time scope. A new classification has been drawn followed by an identification of the challenges of implementing HEVC watermarking techniques based on the analysis and discussion of those chosen articles. Eventually, recommendations for HEVC watermarking techniques have been listed to help researchers to improve the existing techniques or to design new efficient ones.

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Detection of fake high definition for HEVC videos based on prediction mode feature

Cited by 15 publications

References 15 publications

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

HEVC Watermarking Techniques for Authentication and Copyright Applications: Challenges and Opportunities

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