Digital image forensics: a booklet for beginners

By Judith Alice Redi, Wiem Taktak, Jean-Luc Dugelay. Multimedia tools and applications, 2010

366 citations recorded by the source corpus.

Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/9b17b42b071a377380b2f206df483bc9c7a94bac

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Citations in the corpus

Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts (2012, neutral): “…and zooming), rotation, translation, shearing, are applied to an image, pixels within the to-be-transformed image are relocated to a new lattice, and new intensity values have to be assigned to such positions by means of interpolation of the known values, also referred to as resampling operation.”
Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, positive): “By looking at a digital data as a digital clue, multimedia forensics technologies are introducing a novel methodology for supporting clue analysis and providing an aid for making a decision on a crime [1,2].”
MesoNet: a Compact Facial Video Forgery Detection Network (2018, background): “There have been several approaches to detect image forgeries [8, 19], most of them either analyze inconsistencies relatively to what a normal camera pipeline would be or rely on the extraction of specific image alterations in the resulting image.”
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (2019, neutral): “, in recent surveys or books [32, 10].”
An Evaluation of Digital Image Forgery Detection Approaches (2017, background): “These days, because of the headway of computerized picture handling software and altering devices, a picture can be effort-lessly controlled and changed [1].”
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).”