Estimation of Primary Quantization Matrix in Double Compressed JPEG Images

By Jan Lukás. 2003

401 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/70bff3c9e19215c1bca501eb10a51bb63ea6cfe2

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Citations in the corpus

Detection of Double-Compression in JPEG Images for Applications in Steganography (2008, methodology): “Some of the most visible and robust artifacts in the histogram of DCT coefficients arezeros and doublepeaks [6].”
Statistical Tools for Digital Forensics (2004, background): “Double JPEG compression introduces specific artifacts not present in singly compressed images (this observation has also been noted in [17]).”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “A method for estimation of primary quantization matrix from a double compressed JPEG image presented in Fridrich and Lukas (2003).”
Image forgery detection (2009, background): “The presence of these artifacts can, therefore, be used as evidence of some manipulation [25], [35] .”
Media Forensics and DeepFakes: An Overview (2020, background): “local manipulation and is compressed again, double compression artifacts appear all over the image except in the forged region [73].”
A generalized Benford's law for JPEG coefficients and its applications in image forensics (2007, background): “Similar to the phenomenon in the histogram of the double-compressed JPEG image pointed out in [14, 15], when the re-compression quantization step size is exactly integer times of the original compression quantization step size, there is no artifacts shown in the first digit distribution of the doubl”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “Popular targets of such analyses are lens aberration [6], [7], [8], camera response function [9], [10], [11], color filter array (CFA) [12], [13], [14] or JPEG artifacts [15], [16], [17], [18].”
Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact (2007, methodology): “If the tampered image is further JPEG compressed, the primary quantization table could be estimated using method proposed in [11], and then it is used to measure the blocking artifact.”
Information Forensics: An Overview of the First Decade (2013, methodology): “A neural network can be trained to both detect double JPEG compression and estimate the quantization matrix used during the first compression on the basis of an image’s DCT coefficient histograms [50].”
Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis (2009, result): “We shall demonstrate the encouraging results that have been obtained.”
An Overview on Image Forensics (2013, background): “A promising idea is the one introduced by Lukáš and Fridrich in [79]: here, it is proposed to detect the presence of double-aligned JPEG compression by observing that consecutive quantizations introduce periodic artifacts into the histogram of DCT coefficients; these periodic artifacts are visible i”
Digital image forensics: a booklet for beginners (2010, methodology): “Lukas and Fridrich in [63] propose a technique to estimate low-frequency coefficients of the primary quantization table.”
Digital image forensics via intrinsic fingerprints (2008, methodology): “In our future work, we plan to estimate the parameters of such postinterpolation operations as JPEG compression [16] and white balancing,”