Estimating JPEG compression history of bitmaps based on factor histogram

Yang, Jianquan; Zhu, Guopu; Huang, Jiwu; Zhao, Xibin

doi:10.1016/j.dsp.2015.03.014

Cited by 32 publications

(9 citation statements)

References 21 publications

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“…Other seven state-of-the-art methods have been considered for comparisons, that can be grouped into two categories: the threshold-based techniques proposed in [7], [8], [10], [11] and [12] (denoted in the following as BF SINGLE, BLOCK, DCT, NOISE and FACTOR, respectively) and the SVM-based techniques proposed in [6] and [13] (denoted in the following as BF FFT and FSD, respectively). They have been briefly described in Section II and all of them (except for BF SINGLE whose threshold is determined as in [7]) require some preliminary training phase, whose goal is to determine the discriminative threshold to be adopted for the thresholdbased group and the SVM model for the SVM-based group.…”

Section: Resultsmentioning

confidence: 99%

“…We have repeated this operation for three different window sizes and the results, reported in Table VII, show that the proposed method achieves rather good accuracies also when the window size is 128×128, also thanks to its size-adaptive nature. However, when the size of the window further decreases the detection rate decreases as well, generally faster than methods in [11] and [12] which prove to be more suitable for a fine-grained block analysis. The reason for that is two-fold: fewer DCT coefficients are used in the estimation and, being drawn from smaller patches in high resolution images, likely are more correlated to each other, thus not totally fulfilling the independence assumption in the model.…”

Section: Local Analysismentioning

confidence: 99%

“…A similar rationale is exploited in [11], where the authors suggest to use the variance of the forward quantization noise (error in quantizing the DCT coefficients) as discriminative threshold. On the other hand, the work presented in [12] studies the first order statistics of the factors of the AC DCT coefficients, i.e., the set of numbers that divide evenly the coefficients: after the quantization the histogram of the factor set is altered, showing sharper peaks. Thus, the maximum difference between adjacent bins is considered as discriminative threshold.…”

Section: Related Workmentioning

confidence: 99%

“…We would like to thank Dr. Bin Li and Dr. Janquiang Yang for kindly providing us with the code of their methods in [11] and [12], respectively.…”

Section: Acknowledgementmentioning

confidence: 99%

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Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats

Pasquini

Boato

Pérez-González

2017

IEEE Trans.Inform.Forensic Secur.

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Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the 8×8 block-DCT domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of Generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for images of different sizes and source cameras, thus overcoming dataset-dependency issues that typically affect state-of-art techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Local Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We would like to thank Dr. Bin Li and Dr. Janquiang Yang for kindly providing us with the code of their methods in [11] and [12], respectively.…”

Section: Acknowledgementmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats

Pasquini

Boato

Pérez-González

2017

IEEE Trans.Inform.Forensic Secur.

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“…Thai et al [Thai et al 2017] proposed an accurate method for estimating quantization steps from a lossless format image that has experienced JPEG compression. Yang et al [Yang et al 2015] proposed a novel statistic named factor histogram for estimating the JPEG compression history of bitmaps. Li et al [Li et al 2015a] provided a novel quantization noise-based solution to reveal the traces of JPEG compression.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

Deng

Gao

et al. 2019

ACM Trans. Intell. Syst. Technol.

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As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this paper, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF 1) is higher than the second one (QF 2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches. . 2018. Deep multi-scale discriminative networks for double JPEG compression forensics.

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