Detection of HEVC Double Compression With the Same Coding Parameters Based on Analysis of Intra Coding Quality Degradation Process

Jiang, Xinghao; Xu, Qiang; Sun, Tanfeng; Li, Bin; He, Peisong

doi:10.1109/tifs.2019.2918085

Cited by 25 publications

(10 citation statements)

References 20 publications

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“…(2) e potential transcoding process during the network communication is uncontrollable. Besides, for this special case, some existing methods can be used to conduct the supplementary analysis [25].…”

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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“…The technique was able to detect double compression attacks even if the same quantization parameters are used. However, Jiang et al 2019 [44] proposed a similar technique that relies on features extracted from different block sizes instead of 4x4 only, which could achieve better results compared to [7].…”

Section: ) Authentication Challengesmentioning

confidence: 99%

“…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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“…Using 32 YUV video sequences for training and 20 sequences for testing, the highest classification accuracy reported was 96.7%. In [14], a novel method based on the HEVC intra-prediction mode is proposed for detecting double compression. The classification system is based on SVM and uses 136 YUV video clips for training and testing.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to [5,9,13,14], our work uses the HEVC video coding standard but extracts features at both picture and Group-of-Pictures levels. We propose classification systems using machine learning and deep learning with a dataset consisting of 77 YUV video sequences for training and 50 sequences for testing.…”

Section: Introductionmentioning

confidence: 99%

Detecting Double and Triple Compression in HEVC Videos Using the Same Bit Rate

Youssef

Shanableh

2021

SN COMPUT. SCI.

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Digital video forensics refers to the process of analysing, examining, evaluating and comparing a video for use in legal matters. In digital video forensics, the main aim is to detect and identify video forgery to ensure a video's authenticity. When a video is edited, the original bitstream is first decoded, edited and then re-compressed. Therefore, detecting re-compression in videos is a major step in digital video forensics. Video editing can be applied many times leading to multiple compressions. Thus, finding out the compression history of a video becomes an important mean for detecting any manipulation and thereby identifying the legitimacy of a video. In this work, we propose a machine learning approach to detecting double and triple compression in videos coded using the High Efficiency Video Coding (HEVC) format. Feature variables are extracted from Coding Units (CUs) and summarized into picture and Group of Pictures (GoP) feature vectors. Two classifiers are used for classifying videos into single, double and triple compression, namely; Random Forest (RF) and bi-directional Long Short-Term Memory (bi-LSTM). The latter classifier is important in digital video forensics as it exploits the temporal dependencies between feature vectors. In the experimental results, 127 video sequences are used for verifying the accuracy of the proposed solutions. Results are reported in terms of classification accuracy, confusion matrices, precision and recall. The experimental results revealed that both double and triple compression can be accurately detected using the proposed solutions with results superior to existing work.

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Detection of HEVC Double Compression With the Same Coding Parameters Based on Analysis of Intra Coding Quality Degradation Process

Cited by 25 publications

References 20 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

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

Detecting Double and Triple Compression in HEVC Videos Using the Same Bit Rate

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