Identification of bitmap compression history: JPEG detection and quantizer estimation

By Z. Fan, R. Queiroz. IEEE Transactions on Image Processing, 2003

406 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0d139641adfbbcd3bfb729dd53aedc7127e9d8ee

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Citations in the corpus

Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts (2012, methodology): “an idea proposed in [14] to detect blocking artifacts, a 8 × 8 blocking artifact characteristics matrix (BACM) is computed in the pixel domain to measure the symmetrical property of the blocking artifacts in a JPEG image; an asymmetric BACM will reveal the presence of misaligned JPEG compressions.”
A generalized Benford's law for JPEG coefficients and its applications in image forensics (2007, methodology): “To determine whether an image in bitmap format has been JPEG compressed previously or not is an important issue for some image processing applications [12].”
Detection of Double-Compression in JPEG Images for Applications in Steganography (2008, background): “2The problem of detection of previous (single) JPEG compression from bitmap images was also investigated in [3].”
Digital image forensics via intrinsic fingerprints (2008, background): “…Region contains those parts of the image with a significant horizontal gradient for which , where is a suitably chosen threshold; region contains those parts of the image with a significant vertical gradient ; and region includes the remaining parts of the image which primarily contains the…”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Fan and Queiroz (2003) constructed a method determining whether an image has been previously JPEG compressed and to estimate compression parameters.”
Information Forensics: An Overview of the First Decade (2013, methodology): “Additionally, the quantization table used during JPEG compression can be estimated by obtaining a maximum likelihood estimate of the constant spacing between the histogram peaks in each DCT subband [46].”
Image forgery detection (2009, methodology): “The quantization tables can be extracted from the encoded JPEG image or blindly estimated from the image, as described in [6] .”
Media Forensics and DeepFakes: An Overview (2020, background): “discontinuities appear along the block boundaries of compressed images, giving rise to a distinctive and easily detected grid-like pattern [76].”
Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact (2007, methodology): “In addition, our proposed quantization table estimation algorithm is much faster than maximum likelihood based methods [8, 9].”
Estimation of Primary Quantization Matrix in Double Compressed JPEG Images (2003, background): “Previously, successful attempts to determine whether a bitmap image was originally JPEG compressed have been made in [2] and [3].”