Determining the stego algorithm for JPEG images

Pevný, Tomáš; Fridrich, Jessica

doi:10.1049/ip-ifs:20055147

Cited by 22 publications

(29 citation statements)

References 21 publications

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“…For applications in steganalysis, however, it is important to recover compression history from stego images, whose statistics may be disturbed by embedding. The multi-classifier proposed in 14 consists of two separate classifiers and a double-compression detector serving as a pre-classifier. If the double-compression detector decides that an image has been double-compressed, it is sent to the multi-classifier targeted for double-compressed images that can only detect F5 and OutGuess and does not classify to other stego methods recognized by the multi-classifier for single-compressed JPEG images.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images

Pevný

Fridrich

2008

SPIE Proceedings

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A JPEG image is double-compressed if it underwent JPEG compression twice, each time with a different quantization matrix but with the same 8 × 8 grid. Some popular steganographic algorithms (Jsteg, F5, OutGuess) naturally produce such double-compressed stego images. Because double-compression may significantly change the statistics of DCT coefficients, it negatively influences the accuracy of some steganalysis methods developed under the assumption that the stego image was only single-compressed. This paper presents methods for detection of double-compression in JPEGs and for estimation of the primary quantization matrix, which is lost during recompression. The proposed methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images, especially those based on calibration. Both methods rely on support vector machine classifiers with feature vectors formed by histograms of low-frequency DCT coefficients. MOTIVATIONIn this paper, we consider a JPEG image double-compressed if it was compressed twice, each time with a different quantization matrix. The quantization matrix used in the first compression is called the primary quantization matrix, the quantization matrix used in subsequent (second) compression is called the secondary quantization matrix. Since the JPEG image file does not keep information about the compression history, only the latest (secondary) quantization matrix is stored within the file and the primary quantization matrix is lost.Detection of double-compression is important in steganalysis as well as in forensics because the fact that an image was double-compressed indicates that it was manipulated. By determining double-compression history in smaller regions, we may discover traces of malicious manipulation. For example, when pasting an object into a decompressed JPEG and resaving with a different JPEG quality factor, the pasted object may exhibit different repetitive JPEG compression artifacts than the rest of the image. Some steganographic algorithms (e.g., F521 and OutGuess 18 ) decompress the cover image to the spatial domain and then the image is compressed again during embedding with a user supplied or a default quality factor. Unless the quantization matrices match, the resulting stego image will be double-compressed. Thus, steganalytic methods also benefit from knowledge of stego image compression history. This is especially true for methods that use calibration 5 to estimate the statistics of the cover image. It is absolutely essential to adjust the calibration to mimic what happened during embedding. To do so, we need to accurately detect double-compressed images and estimate their primary quantization matrix, otherwise the steganalytic methods may give completely misleading results. 5In this paper, we address two problems: the detection of double-compression in JPEG images and the estimation of primary quantization matrix. Even though the first problem can be understood as a subproblem of the second one, we consider them separately. This...

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Section: The Proposed Methodsmentioning

confidence: 99%

Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images

Pevný

Fridrich

2008

SPIE Proceedings

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“…The resultant distortions cause due to embedding in the cover image can be analysed by comparing the statistical properties of both cover and stegoimages 8,13 . Several techniques are available to detect such changes based on first order statistical distributions of intensity or transform coefficients 13,16 .…”

Section: Feature Extraction Using Higher Order Image Statisticsmentioning

confidence: 99%

“…Several steganalysis approaches [6][7][8][9] have been proposed which can broadly be classified into four categories: Supervised learning-based steganalysis 10,11 , blind identification-based steganalysis 7 , parametric statistical steganalysis 9,12,13 and hybrid techniques 7 . Supervised learning-based steganalysis techniques involve two phases: (a) training phase and (b) testing phase.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis

Rajput

Agrawal

Aggrawal

2012

DSJ

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The performance of supervised learning-based seganalysis depends on the choice of both classifier and features which represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalisation. Linear discriminant classifier which is commonly used for classification may not be able to classify in better way non-linearly separable data. Recently, exponential discriminant analysis, a variant of linear discriminant analysis (LDA), is proposed which transforms the scatter matrices to a new space by distance diffusion mapping. This provides exponential discriminant analysis (EDA) much more discriminant power to classify non-linearly separable data and helps in improving classification accuracy in comparison to LDA. In this paper, the performance of EDA in conjunction with feature selection methods has been investigated. For feature selection, Kullback divergence, Chernoff distance measures and linear regression measures are used to determine relevant features from higher-order statistics of images. The performance is evaluated in terms classification error and computation time. Experimental results show that exponential discriminate analysis in conjunction with linear regression significantly performs better in terms of both classification error and compilation time of training classifier.

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“…Second, it is possible to extend the multi-classifier to other quality factors without having to change the classifiers that were already trained. Although it is possible to use a multi-classifier to classify images with quality factors different from the one the multi-classifier was trained for, the accuracy of classification decreases [25].…”

Section: Multi-classifiermentioning

confidence: 99%

“…The tables drive the quantization of DCT coefficients and thus change their statistical properties. This effectively enlarges the space of covers and further complicates steganalysis because a classifier trained on one quality factor may give less accurate results on images with a different quality factor (see, e.g., Table 3 and 4 in [25]). Second, multiple JPEG compression may dramatically change the statistics of DCT coefficients and thus cause some steganalysis methods to fail [7].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Detector of Current Steganographic Methods for JPEG Format

Pevný

Fridrich

2008

IEEE Trans.Inform.Forensic Secur.

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Abstract-The aim of this paper is to construct a practical forensic steganalysis tool for JPEG images that can properly analyze both single-and double-compressed stego images and classify them to selected current steganographic methods. Although some of the individual modules of the steganalyzer were previously published by the authors, they were never tested as a complete system. The fusion of the modules brings its own challenges and problems whose analysis and solution is one of the goals of this paper.By determining the stego algorithm, this tool provides the first step needed for extracting the secret message. Given a JPEG image, the detector assigns it to 6 popular steganographic algorithms. The detection is based on feature extraction and supervised training of two banks of multi-classifiers realized using support vector machines. For accurate classification of single-compressed images, a separate multi-classifier is trained for each JPEG quality factor from a certain range. Another bank of multiclassifiers is trained for double-compressed images for the same range of primary quality factors. The image under investigation is first analyzed using a pre-classifier that detects selected cases of double-compression and estimates the primary quantization table. It then sends the image to the appropriate single-or double-compression multiclassifier. The error is estimated from more than 2.6 million images. The steganalyzer is also tested on two previously unseen methods to examine its ability to generalize.

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Determining the stego algorithm for JPEG images

Cited by 22 publications

References 21 publications

Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images

Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images

Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis

Multiclass Detector of Current Steganographic Methods for JPEG Format

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