Exposing digital forgeries by detecting inconsistencies in lighting

By Micah K. Johnson, Hany Farid. Workshop on Multimedia & Security, 2005

473 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/995e3aad6cd659bbba01e545c36efaf71a04fbb4

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Citations in the corpus

Digital image forensics: a booklet for beginners (2010, background): “Farid in [46], in which they estimate the incident light direction for different objects in order to highlight mismatches.”
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection (2021, unknown): “The differences are calculated by varied approaches, including media-format based compression artifacts [6], [7], physics-based lighting inconsistency [8], [9], statistical modeling [10], local noise estimation [11], etc.”
Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact (2007, background): “The second attempt is based on the statistics of natural image [5], inconsistencies based on scene lighting direction [6], or camera response normality [7].”
Determining Image Origin and Integrity Using Sensor Noise (2008, background): “We now explain how the shaping factor and variance are estimated.”
Using noise inconsistencies for blind image forensics (2009, background): “…The Institute of Information Theory and Automation of the ASCR, Pod Vodárenskou věží 4, 182 08 Prague 8, Czech Republic a r t i c l e i n f o Article history: Received 3 October 2007 Received in revised form 30 January 2009 Accepted 2 February 2009 Keywords: Image forensics Digital forgery…”
An Overview on Image Forensics (2013, background): “e �rst issue when trying to �nd the light source direction in a scene is that it is not easy to extract threedimensional (3D) surface normals from a single image; in [131], a simplifying solution is proposed: only the 2D surface normals at the occluding object boundary are considered, so that only ”
Digital image forgery detection using passive techniques: A survey (2013, background): “Recent image forgeries reported (a) Composite of Cher and Brad Pitt (Johnson and Farid, 2005 ) (f) Time covers reporting on the O.J. Simpson case (Redi et al., 2011).”
Exposing Digital Forgeries in Complex Lighting Environments (2007, background): “We have previously shown how to estimate the direction to a light source, and how inconsistencies in the illuminant direction can be used to detect tampering [9], [10].”
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].”
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.”
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].”
Scaling Out-of-Distribution Detection for Real-World Settings (2022, methodology): “Techniques for detecting image manipulation (Zhou et al., 2018; Johnson & Farid, 2005) are competent at detecting artificial image elements of this kind.”
Detecting digital image forgeries using sensor pattern noise (2006, background): “These components are known as “doughnut” patterns and vignettage and are of low spatial frequency in nature [11].”
Information Forensics: An Overview of the First Decade (2013, background): “In early work, this was accomplished this by obtaining a two dimensional estimate of the direction of the light source illuminating an object [69].”
Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency (2006, methodology): “Farid et al have developed techniques for spliced image detection by object lighting inconsistency [2].”
Detection of copy-move forgery using a method based on blur moment invariants. (2007, methodology): “Existing digital forgery detection methods are divided into active [2–6], and passive (blind) [7–10] approaches.”