Photo forensics from JPEG dimples

By Shruti Agarwal, Hany Farid. 2017

73 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Law

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Citations in the corpus

CNN-Generated Images Are Surprisingly Easy to Spot… for Now (2019, background): “Early work focused on hand-crafted cues [16] such as compression artifacts [5], resampling [37], or physical scene constraints [32].”
On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, background): “Note that JPEG compression actually varies across libraries, which has been exploited for image forensics applications [2, 3].”
Fighting Fake News: Image Splice Detection via Learned Self-Consistency (2018, methodology): “Since [16] reported their numbers on the full Columbia and Carvalho datasets (rather than our test split), we evaluated our methods on the full dataset and report the comparison in Table 3.”
Towards Universal Fake Image Detectors that Generalize Across Generative Models (2023, methodology): “When traditional methods are used to manipulate an image, the alteration in the underlying image statistics can be detected using hand-crafted cues such as compression artifacts [5], resampling [40] or irregular reflections [37].”
Media Forensics and DeepFakes: An Overview (2020, methodology): “In [81] model-specific JPEG features have been defined, the JPEG”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “Popular targets of such analyses are lens aberration [5]–[7], camera response function [8]–[10], color filter array (CFA) [11]–[13] or JPEG artifacts [14]–[16].”
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection (2018, background): “Traditional Media Forensics Traditional model-based approaches exploit a specific clue, like inconsistencies at pixel-level related for example to JPEG compression artifacts [4], demosaicking clues [16], lens aberration [41], or camera noise [29].”
Detecting Photoshopped Faces by Scripting Photoshop (2019, background): “There has also been work that detects inconsistent quantization [2], double-JPEG artifacts [6, 3], and geometric inconsistencies [25].”
Photo Forensics From Rounding Artifacts (2020, background): “It has recently been shown that artifacts introduced by the choice of rounding operator used to quantize the DCT coeicients can be used to localize tampering and identify speciic encoders [1, 9].”