The authenticity and reliability of digital images are increasingly important due to the ease in
modifying such images. Thus, the capability to identify image manipulation is a current …

With advanced image journaling tools, one can easily alter the semantic meaning of an
image by exploiting certain manipulation techniques such as copy clone, object splicing …

With the advent of Internet, images and videos are the most vulnerable media that can be
exploited by criminals to manipulate for hiding the evidence of the crime. This is now easier …

We introduce a novel deep neural architecture for image copy-move forgery detection
(CMFD), code-named BusterNet. Unlike previous eorts, BusterNet is a pure, end-to-end …

The emergence of powerful image editing software has substantially facilitated digital image
tampering, leading to many security issues. Hence, it is urgent to identify tampered images …

In this paper, we improve the parallel deep neural network (DNN) scheme BusterNet for
image copy-move forgery localization with source/target region distinguishment. BusterNet …

Copy Move Forgery Detection (CMFD) is helpful to detect copied and pasted areas in one
image, it plays a crucial role in legal evidence, forensic investigation and in many more …

A novel image copy-move forgery detection scheme using a Dense-InceptionNet is
proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense …

Image editing techniques enable people to modify the content of an image without leaving
visual traces and thus may cause serious security risks. Hence the detection and localization …

Editing a real-world photo through computer software or mobile applications is one of the
easiest things one can do today before sharing the doctored image on one's social …