Exposing Digital Forgeries From JPEG Ghosts

By Hany Farid. IEEE Transactions on Information Forensics and Security, 2009

482 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6345039a2e574987a7fc4bb9032878812de598a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

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].”
An Overview on Image Forensics (2013, background): “Similarly to [85], themain idea relies on the fact that in case the image has been compressed twice aer tampering and the analyst identi�es the right quantization steps of the �rst compression, most parts of the reconstructed image result to be highly correlated with the analyzed image.”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “Several approaches exploit JPEG blocking artifacts to detect tampered regions [12], [13], [27], [52], [57].”
Information Forensics: An Overview of the First Decade (2013, background): “A number of forensic techniques have been designed to identify cut-and-paste image forgeries by detecting localized inconsistencies in DCT domain double JPEG compression fingerprints [56]–[61].”
Media Forensics and DeepFakes: An Overview (2020, background): “called JPEG ghosts [77], arising in the manipulated area when two JPEG compressions use the same quality factor (QF).”
Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts (2012, methodology): “In [8], an improved version of the model proposed in [7] is presented, leading to a significant improvement of the accuracy of the probability map estimation and consequently of the algorithm performance.”
Digital image forensics: a booklet for beginners (2010, background): “[31] showed how to reveal whether different zones of”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Several surveys have been published on image forgery detection: Rocha et al. (2011), Farid (2009a), Mahdian and Saic (2010), Lanh et al. (2007a), Luo et al. (2007a), Mahdian and Saic (2008a), Ng et al. (2006), Sencar and Memon (2008), Zhang et al. (2008a), Bayram et al. (2008a) and Redi et al.…”