Exposing Digital Forgeries in Complex Lighting Environments

By Micah K. Johnson, Hany Farid. IEEE Transactions on Information Forensics and Security, 2007

362 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6b9182f5388829f4df88b3b9ba1e3b8619f27710

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

Information Forensics: An Overview of the First Decade (2013, methodology): “To address this, a more advanced method exists to estimate the locations of multiple light sources in a scene [70].”
Digital image forensics: a booklet for beginners (2010, background): “In a later work [48] the same authors present a more complex lighting environment model, exploiting spherical harmonics for its representation.”
Forensic detection of image manipulation using statistical intrinsic fingerprints (2010, background): “Previous image forensic work has dealt with the identification of computer generated objects within an image [2] as well as detecting lighting angle inconsistencies [3], [4].”
Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement (2015, background): “Forgery in photographs in which, for example, a person is inserted is exposed by Johnson and Farid (2007) by analysing the lighting.”
PhotoProof: Cryptographic Image Authentication for Any Set of Permissible Transformations (2016, background): “1 Forensic experts can seek anomalies in content, such as shadow/illumination direction [29], in file metadata,”
An Overview on Image Forensics (2013, background): “Again, the same authors consider the presence of multiple light sources, diffuse lighting or directional lighting, in [133], where they try to estimate the lighting environment taking some simplifying hypothesis (i.”
Region Duplication Detection Using Image Feature Matching (2010, background): “inconsistency in the lighting geometry [23] may be used to locate duplicated regions.”
Media Forensics and DeepFakes: An Overview (2020, background): “for example shadows or illumination or perspective [3], [4], [5].”
Exposing Digital Forgeries From JPEG Ghosts (2009, methodology): “These include techniques for detecting cloning [1], [2]; splicing [3]; re-sampling artifacts [4], [5] ; color filter array aberrations [6]; disturbances of a camera’s sensor noise pattern [7]; chromatic aberrations [8]; and lighting inconsistencies [9], [10], [11].”
Digital Image Authentication From JPEG Headers (2011, background): “…to detect various traces of tampering: region duplication [1]–[4]; resampling [5], [6]; color filter array artifacts [7], [8]; inconsistencies in camera response function [9]; inconsistencies in lighting and shadows [10]–[13]; inconsistencies in chromatic aberrations [14], [15]; inconsistencies in…”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “Some of them rely on semantic or physical inconsistencies [3], [4],”