HEVC Double Compression Detection with Non-Aligned GOP Structures Based on a Fusion Feature with Optical Flow and Prediction Units

Wu, Qiyuan; Sun, Tanfeng; Jiang, Xinghao; Xu, Qiang; He, Peisong

doi:10.1109/cisp-bmei48845.2019.8965766

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…13 and some P frames to be reencoded as P frames (P-P frames). When compared to neighbouring P-P frames, relocated I-P frames possess abnormal characteristics in basic coding elements, such as variation in statistics of CU, PU and TU; disproportion of intra, inter and skip units; and disparity in Intra modes has been observed [26][27][28][29][30][31]. Authors in [26] counted the number of intra, inter and skip PUs and designed a feature vector named, SN-PUPM, by taking absolute difference with adjacent three frames.…”

Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

“…Further, Wu et al [28] combined the count of inter PUs with optical flow residuals to make a fusion feature. Their technique improved the detection of relocated I-P frames by 4.59% in comparison to the SNPUPM feature [26].…”

Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

“…Jia et al [33] proposed an approach utilizing the count of 4x4 PUs in each I-frame and computed standard deviation between the single compressed and double compressed versions of the video. Experiments were conducted for QP (22,24,26,28,32) and an appropriate threshold was selected for detection results. An accuracy of 82.22% was achieved.…”

Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

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A Review on HEVC Video Forensic Investigation under Compressed Domain

Singla¹,

Nagpal²,

Singh³

2022

IJIGSP

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In recent years, video forensic investigation has become a prominent research area, due to the adverse effect of fake videos on networks, people and society. This paper summarizes all the existing methodologies used for forgery detection in H.265/HEVC videos. HEVC video forgery is generally classified into two categories as video quality forgery and video content forgery. The occurrence of various forgeries such as transcoding, fake-bitrate, inter-frame forgery and intra-frame forgery is deeply analyzed based on features extracted from the HEVC compression domain. The major findings of this research are (i) Less focus on transcoding detection, (ii) Non-availability of HEVC forged video dataset (iii) More focus on double compression detection for forgery detection, and (iv) Non-consideration of adaptive-GOP structure. The forgery detection in the video is critically important due to its wide use as the primary source of information in criminal investigations and proving the authenticity of contents. So, the forgery detection accuracy is of major concern at the present time. Although, various forgery detection methods are developed in past but the findings of this review point out the need of developing more effective detection methods with high accuracy.

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Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

Section: Double Compression: Non-aligned Gopmentioning

confidence: 99%

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A Review on HEVC Video Forensic Investigation under Compressed Domain

Singla¹,

Nagpal²,

Singh³

2022

IJIGSP

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“…However, traces in decompression domain are easier to be degraded by the severe lossy quantization in the reencoding process. More recently, for HEVC videos, artifacts in both compression domain (e.g., PU types) and decompression domain (optical flow) are combined to expose relocated I-frames [20]. Global average pooling and 1×1 convolutional kernel were considered in the network architecture to mitigate the influence of overfitting.…”

Section: 1mentioning

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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