Statistical Model of Quantized DCT Coefficients: Application in the Steganalysis of Jsteg Algorithm

Thai, Thanh Hai; Cogranne, Rémi; Retraint, Florent

doi:10.1109/tip.2014.2310126

Cited by 68 publications

(71 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main motivation is that the statistics of DCT coefficients change with different sensor noises combining with various in-camera processing algorithms. Therefore, this paper exploits a state-of-the-art model of DCT coefficients [29] to accurately extract information at different frequencies and proposes a new fingerprint for camera model identification.…”

Section: Main Contributions Of the Papermentioning

confidence: 99%

“…• This paper is based on the state-of-the-art statistical model of DCT coefficients [27][28][29] for fingerprint extraction. The parameters (c, d) that characterize the simplified linear relation between two parameters α and β −1 , which are specified in the proposed model of DCT coefficients, are exploited as camera fingerprint for camera model identification.…”

Section: Main Contributions Of the Papermentioning

confidence: 99%

See 1 more Smart Citation

Camera model identification based on DCT coefficient statistics

Thai

Retraint

Cogranne

2015

Digital Signal Processing

Self Cite

View full text Add to dashboard Cite

The goal of this paper is to design a statistical test for the camera model identification problem from JPEG images. The approach focuses on extracting information in Discrete Cosine Transform (DCT) domain. The main motivation is that the statistics of DCT coefficients change with different sensor noises combining with various in-camera processing algorithms. To accurately capture this information, this paper relies on the state-of-the-art model of DCT coefficients proposed in our previous work. The DCT coefficient model is characterized by two parameters (α, β). The parameters (c, d) that characterize the simplified relation between these parameters are exploited as camera fingerprint for camera model identification. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its performances are theoretically established. For a practical use, two Generalized Likelihood Ratio Tests are designed to deal with unknown model parameters such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated and real JPEG images highlight the relevance of the proposed approach.

show abstract

Section: Main Contributions Of the Papermentioning

confidence: 99%

Section: Main Contributions Of the Papermentioning

confidence: 99%

Camera model identification based on DCT coefficient statistics

Thai

Retraint

Cogranne

2015

Digital Signal Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such a result can be explained by the two following reasons: 1) the Laplacian model might be not accurate enough to detect steganagraphy and 2) the assumption that the DCT coefficients of each frequency subband are i. i. d. may be wrong. Recently, it has been shown that the use of the generalised gamma model or an even more accurate model [36,37] allows the designing of a test with very good detection performance. On the opposite, in this paper, it is proposed to challenge the assumption that all the DCT coefficients of a subband are i. i. d.…”

Section: Problem Statementmentioning

confidence: 99%

“…This theorem is of crucial interest to establish the statistical properties of the proposed test [7,22,37,38]. In fact, once the moments have been calculated under both H i , i = {0, 1}, one can normalise under hypothesis H 0 the LR lr (U) as follows:…”

Section: Statistical Performance Of Lrtmentioning

confidence: 99%

Steganalysis of JSteg algorithm using hypothesis testing theory

Qiao

Retraint

Cogranne

et al. 2015

EURASIP J. on Info. Security

Self Cite

View full text Add to dashboard Cite

This paper investigates the statistical detection of JSteg steganography. The approach is based on a statistical model of discrete cosine transformation (DCT) coefficients challenging the usual assumption that among a subband all the coefficients are independent and identically distributed (i. i. d. ). The hidden information-detection problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented, and its performances are theoretically established. The statistical performance of LRT serves as an upper bound for the detection power. For a practical use where the distribution parameters are unknown, by exploring a DCT channel selection, a detector based on estimation of those parameters is designed. The loss of power of the proposed detector compared with the optimal LRT is small, which shows the relevance of the proposed approach.

show abstract

“…Detectors from the first category can be guaranteed to be optimal within the chosen cover model and thus designed to maximize the detection power for a prescribed false-alarm probability. However, they are limited to steganographic schemes that introduce easily detectable artifacts into the statistics of DCT coefficients, such as Jsteg 18,22 or OutGuess. 19 However, modern steganographic schemes, example of which is J-UNIWARD 11 and Uniform Embedding Distortion (UED), 5 and many side-informed schemes, such as SI-UNIWARD, 11 Normalized Perturbed Quantization (NPQ), 12 and Entropy-Block Steganography (EBS), 20 do not introduce easily detectable artifacts into the statistics of quantized DCT coefficients.…”

Section: Introductionmentioning

confidence: 99%

Phase-aware projection model for steganalysis of JPEG images

Holub

Fridrich

2015

SPIE Proceedings

View full text Add to dashboard Cite

State-of-the-art JPEG steganographic algorithms, such as J-UNIWARD, are currently better detected in the spatial domain rather than the JPEG domain. Rich models built from pixel residuals seem to better capture the impact of embedding than features constructed as co-occurrences of quantized JPEG coefficients. However, when steganalyzing JPEG steganographic algorithms in the spatial domain, the pixels' statistical properties vary because of the underlying 8 × 8 pixel grid imposed by the compression. In order to detect JPEG steganography more accurately, we split the statistics of noise residuals based on their phase w.r.t. the 8 × 8 grid. Because of the heterogeneity of pixels in a decompressed image, it also makes sense to keep the kernel size of pixel predictors small as larger kernels mix up qualitatively different statistics more, losing thus on the detection power. Based on these observations, we propose a novel feature set called PHase Aware pRojection Model (PHARM) in which residuals obtained using a small number of small-support kernels are represented using first-order statistics of their random projections as in the projection spatial rich model PSRM. The benefit of making the features "phase-aware" is shown experimentally on selected modern JPEG steganographic algorithms with the biggest improvement seen for J-UNIWARD. Additionally, the PHARM feature vector can be computed at a fraction of computational costs of existing projection rich models.

show abstract

Statistical Model of Quantized DCT Coefficients: Application in the Steganalysis of Jsteg Algorithm

Cited by 68 publications

References 36 publications

Camera model identification based on DCT coefficient statistics

Camera model identification based on DCT coefficient statistics

Steganalysis of JSteg algorithm using hypothesis testing theory

Phase-aware projection model for steganalysis of JPEG images

Contact Info

Product

Resources

About