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

07/25/2015
by   Minati Mishra, et al.
0

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.

READ FULL TEXT

page 2

page 6

page 7

page 8

research
06/28/2013

Digital Image Tamper Detection Techniques - A Comprehensive Study

Photographs are considered to be the most powerful and trustworthy media...
research
08/24/2022

Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools

Digital media (e.g., photographs, video) can be easily created, edited, ...
research
12/06/2017

DCT-domain Deep Convolutional Neural Networks for Multiple JPEG Compression Classification

With the rapid advancements in digital imaging systems and networking, l...
research
09/11/2018

Intensity and Rescale Invariant Copy Move Forgery Detection Techniques

In this contemporary world digital media such as videos and images behav...
research
11/25/2022

Training Data Improvement for Image Forgery Detection using Comprint

Manipulated images are a threat to consumers worldwide, when they are us...
research
04/05/2023

JPEG Compressed Images Can Bypass Protections Against AI Editing

Recently developed text-to-image diffusion models make it easy to edit o...
research
08/22/2021

Detection and Localization of Multiple Image Splicing Using MobileNet V1

In modern society, digital images have become a prominent source of info...

Please sign up or login with your details

Forgot password? Click here to reset