Aug 10, 2021 · This paper, presents an efficient method to detect copy move forgery detection in medical images using center symmetric local binary pattern (CSLBP) which is able to detect the...

Jun 20, 2020 · Copy Move Forgery is basically cloning/copying a part of the image and then moving it to the other location to hide some details or to produce some fake information.

The Image Forgery Detection project demonstrates the effectiveness of machine learning techniques, specifically SVM classifiers, in identifying instances of digital image forgery. With a focus on Copy-Move forgery, the system achieves high accuracy rates and provides a reliable solution for image authenticity verification.

In this paper, we use an improved algorithm based on Singular Value Decomposition (SVD) to detect this image forgery. In this method after applying image pre processing operations the image is divided in number of overlapping blocks. The SV features are extracted from each block.

A new deep learning-based method to detection of copy-move forgery in digital images; Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT); Istanbul, Turkey. 24–26 April 2019; pp. 1–4.

We propose a method copy-move image forgery detection using feature point extraction and morphological operations. The proposed scheme integrates both block-based and key point-based forgery detection methods. First, segments the host image into non-overlapping and irregular blocks adaptively.

Jun 13, 2019 · In this study, the major strategy of our proposed algorithm focuses on a single tampered region detection. And we have proposed keypoint-based image forensics for copy-move forgery images based on a Helmert transformation and SLIC superpixel segmentation.

An innovative approach for image forgery detection by utilizing a DenseNet-201 convolutional neural network is presented. The proposed method utilizes DenseNet-201 architecture to extract features from an input image and then uses a fully connected layer to classify the image as either genuine or forged.

Our method uniquely integrates these architectures to effectively detect and analyze multiple types of image forgeries, including image splicing and copy-move forgeries. This approach is groundbreaking as it adapts these networks to focus on identifying discrepancies in the compression quality between forged and original image regions.

Apr 30, 2024 · In this paper, we present an original technique for copy-move image forgery detection that combines Scale-Invariant Feature Transform (SIFT) with Convolutional Neural Networks . Our approach uses SIFT for reliable key-point descriptor extraction and Error Level Analysis (ELA) preprocessing to improve potentially changed regions.