Robust Detection of Intensity Variant Clones in Forged and JPEG Compressed Images

TL;DR

This paper introduces a novel intensity invariant detection model (IIDM) that effectively identifies intensity variant clones in forged images, maintaining robustness against JPEG compression, noise, and blurring.

Contribution

The paper presents a new intensity invariant detection model (IIDM) specifically designed for robust clone detection in manipulated images.

Findings

IIDM is robust against JPEG compression.

IIDM effectively detects clones under noise attacks.

IIDM performs well with blurred images.

Abstract

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.