Forensics for partially double compressed doctored JPEG images

Singh, Gurinder; Singh, Kulbir

doi:10.1007/s11042-016-4290-5

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…According to [18], the DCT coefficients p(x) at a frequency (r, c) of a tampered JPEG image can be modeled as the mixture of a tampered component (x; 2 ) and an authentic component (x; 1 , 2 ) as: Obviously, if the quantization noise can be eliminated, a better mixture model of the DCT coefficients histogram will be obtained, which can improve the performance. In order to solve this problem, we first resort to the split noise filtering algorithm [25] to eliminate the split noise. In this algorithm, the authors first traversed the bins of the input histogram and gathered the bins introduced by the split noise according to Equation (6).…”

Section: Tampered Region Localizationmentioning

confidence: 99%

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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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Section: Tampered Region Localizationmentioning

confidence: 99%

“…Then, the bins introduced by the split noise were removed by moving these bins to the nearest neighboring bins. More detailed descriptions can be found in [25]. Once the split noise has been removed, the residual noise can be easily removed with a proper threshold T filter as it is very small.…”

Section: Tampered Region Localizationmentioning

confidence: 99%