Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks

Yao, Heng; Song, Saihua; Qin, Chuan; Tang, Zhenjun; Liu, Xiaokai

doi:10.3390/sym9120313

Cited by 13 publications

(3 citation statements)

References 23 publications

(44 reference statements)

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“…3, a comparison between the theoretical and the empirical distribution is given under H 0 and H 1 . One can see how the empirical distributions match the theoretical model given in (13).…”

Section: Numerical Experimentation a Model Validationmentioning

confidence: 56%

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Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

et al. 2022

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With the 2019 Coronavirus pandemic, we have seen an increasing use of remote technologies such has remote identity verification. The authentication of the user identity is often performed through a biometric matching of a selfie and a video of an official identity document. In such a scenario, it is essential to verify the integrity of both the selfie and the video. In this article, we propose a method to detect double video compression in order to verify the video integrity. We will focus on the H.264 compression which is one of the mandatory video codecs in the WebRTC Requests For Comments. H.264 uses an integer approximation of the Discrete Cosine Transform (DCT). Our method focuses on the DCT coefficients to detect a double compression. The coefficients roughly follow a Laplacian distribution, we will show that the distribution parameters vary with respect to the quantisation parameter used to compress the video. We thus propose a statistical hypothesis test to determine whether or not a video has been compressed twice.INDEX TERMS Video forensics, double compression, DCT, H.264, hypothesis testing.

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“…3, a comparison between the theoretical and the empirical distribution is given under H 0 and H 1 . One can see how the empirical distributions match the theoretical model given in (13).…”

Section: Numerical Experimentation a Model Validationmentioning

confidence: 56%

“…In [12], authors trained a One-Class classifier on the reconstructed frame residual to detect the double compression. In [13], the authors directly study the bit size of each encoded frame. They showed that relocated I-frame requires more bits than typical P or B-frame and can thus be detected.…”

Section: A State Of the Artmentioning

confidence: 99%

Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

et al. 2022

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“…Besides, some novel image segmentation technologies can be considered for locating the tampered region more precisely and accelerating our method. Finally, the double compression detection tasks on some other media such as audio [28] and video [29,30] will be considered in the future.…”

Section: Discussionmentioning

confidence: 99%

Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model

Zhu

Shen²,

Niu³

2019

Symmetry

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With the wide use of various image altering tools, digital image manipulation becomes very convenient and easy, which makes the detection of image originality and authenticity significant. Among various image tampering detection tools, double JPEG image compression detector, which is not sensitive to specific image tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT (Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the block-wise DCT coefficients histogram and eliminate the quantization noise which introduced by rounding and truncation operations. Then, for each DCT frequency, a posterior probability can be obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally, the probabilities from all the DCT frequencies are accumulated to give the posterior probability of a DCT block being authentic or tampered. Extensive experimental results in both quantitative and qualitative terms prove the superiority of our proposed method when compared with the state-of-the-art methods.

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