Robust Detection of Intensity Variant Clones in Forged and JPEG Compressed Images
This addresses the threat of digital image forgery to media authenticity, offering a detection method for professionals and users, but it appears incremental as it builds on existing forgery detection research.
The paper tackles the problem of detecting forged images by introducing an intensity invariant detection model (IIDM) that identifies intensity variant clones, achieving robustness against JPEG compression, noise attacks, and blurring.
Digitization of images has made image editing easier. Ease of image editing tempted users and professionals to manipulate digital images leading to digital image forgeries. Today digital image forgery has posed a great threat to the authenticity of the popular digital media, the digital images. A lot of research is going on worldwide to detect image forgery and to separate the forged images from their authentic counterparts. This paper provides a novel intensity invariant detection model (IIDM) for detection of intensity variant clones that is robust against JPEG compression, noise attacks and blurring.