Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact

By Shuiming Ye, Qibin Sun, Ee-Chien Chang. IEEE International Conference on Multimedia and Expo, 2007

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a7d0c3e16a4ede99e36a45eee4a6c70ec2e3f414

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

An Overview on Image Forensics (2013, methodology): “In [72], the authors propose a method for estimating the elements of thewhole quantization table.”
Photo forensics from JPEG dimples (2017, methodology): “…(DCT) coefﬁcients are used to identify the recording camera manufacturer and model [2]–[4]; anomalies in the distribution of DCT coefﬁcients are used to reveal multiple JPEG compressions [5]–[7]; and artifacts introduced by the JPEG blocking are used to detected localized tampering [8]–[10].”
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.”
Digital image forgery detection using passive techniques: A survey (2013, background): “A passive approach to detect digital forgeries by checking image quality inconsistencies based on blocking artifact caused by JPEG compression was suggested by Ye et al. (2007).”
Photo Forensics From Rounding Artifacts (2020, methodology): “A variety of such forensic techniques have been developed based on the JPEG compression format: The quantization values used to quantize the discrete cosine transform (DCT) coeicients are used to identify the recording camera manufacturer and model [16, 22, 23]; anomalies in the distribution of DCT ”
Fighting Fake News: Image Splice Detection via Learned Self-Consistency (2018, methodology): “We compared our model with three methods that use image processing techniques to detect specific imaging artifacts: Color Filter Array (CFA) [44] detects artifacts in color pattern interpolation; JPEG DCT [45] detects inconsistencies over JPEG coefficients; and Noise Variance (NOI) [46] detects anom”
Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (2017, methodology): “Most of these methods use features from one of two subgroups: JPEG quantization artifacts and JPEG compression grid discontinuities [23, 5, 6, 14, 22, 36, 25, 1, 4].”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “They can be roughly classiﬁed in three classes according to the features they exploit: i) JPEG artifacts [60], [61], [62], [63], [16], [64], ii) CFA artifacts [14], [13], iii) inconsistencies in the spatial distribution of features [65], [26], [66], [27], [28], [58].”