Review on tools for image detection forgery

This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Fake images have become widespread in society today. The accessibility to powerful simple to use image editing computer software to end users helps make the job of manipulating image incredibly easy. One can find forged images used to sensationalize news, spread political propaganda and rumors, introduce psychological bias, etc. in all forms of media.

[1]  Jessica Fridrich,et al.  Detection of Copy-Move Forgery in Digital Images , 2004 .

[2]  Babak Mahdian,et al.  A bibliography on blind methods for identifying image forgery , 2010, Signal Process. Image Commun..

[3]  Husrev T. Sencar,et al.  Overview of State-of-the-Art in Digital Image Forensics , 2007 .

[4]  Gregory A. Baxes,et al.  Digital image processing - principles and applications , 1994 .

[5]  Subhasis Saha,et al.  Image compression—from DCT to wavelets: a review , 2000, CROS.

[6]  Wei Lu,et al.  Digital image forensics using statistical features and neural network classifier , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[7]  Ricardo L. de Queiroz,et al.  Identification of bitmap compression history: JPEG detection and quantizer estimation , 2003, IEEE Trans. Image Process..

[8]  Babak Mahdian,et al.  Blind methods for detecting image fakery , 2010 .

[9]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005 .

[10]  Digital Image Basics , .

[11]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[12]  Siwei Lyu,et al.  Higher-order Wavelet Statistics and their Application to Digital Forensics , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[13]  Matthias Kirchner,et al.  Linear row and column predictors for the analysis of resized images , 2010, MM&Sec '10.

[14]  Babak Mahdian,et al.  Ieee Transactions on Information Forensics and Security 1 Blind Authentication Using Periodic Properties of Interpolation , 2022 .