Journal ArticleDOI
An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction
Sreenivasu Tinnathi,G. Sudhavani +1 more
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TLDR
A new CMFD approach is proposed on the basis of both block and keypoint based approaches that outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2021-01-01. It has received 28 citations till now. The article focuses on the topics: Feature extraction & RANSAC.read more
Citations
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Journal ArticleDOI
Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics.
TL;DR: In this paper, the authors proposed two approaches that use deep learning, one by a custom architecture and a model by transfer learning, to solve the copy-move problem in image and video editing.
Journal ArticleDOI
Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection
TL;DR: This article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC), which combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets).
Journal ArticleDOI
A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions
Journal ArticleDOI
QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method
TL;DR: Mehrad Aria et al. as discussed by the authors proposed a dual-branch CNN architecture consisting of two subnetworks, namely a manipulation detection sub-network and a similarity detection subnetwork.
Journal ArticleDOI
Copy move forgery detection and segmentation using improved mask region-based convolution network (RCNN)
TL;DR: Wang et al. as discussed by the authors proposed a Mask-RCNN model with the DenseNet-41 as the base network which is capable of nominating a better set of image features and presents the complex image transformation effectively.
References
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Journal ArticleDOI
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
TL;DR: The problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward.
Journal ArticleDOI
Segmentation-Based Image Copy-Move Forgery Detection Scheme
TL;DR: The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction, and the copy-move regions can be detected by matching between these patches.
Journal ArticleDOI
An Evaluation of Popular Copy-Move Forgery Detection Approaches
TL;DR: This paper created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation, and examined the 15 most prominent feature sets, finding the keypoint-based features Sift and Surf as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well.
Journal ArticleDOI
Region Duplication Detection Using Image Feature Matching
Xunyu Pan,Siwei Lyu +1 more
TL;DR: The proposed method starts by estimating the transform between matched scale invariant feature transform (SIFT) keypoints, which are insensitive to geometrical and illumination distortions, and then finds all pixels within the duplicated regions after discounting the estimated transforms.
Book ChapterDOI
Detection of copy-rotate-move forgery using Zernike moments
TL;DR: A detection method of copy-move forgery that localizes duplicated regions using Zernike moments that can detect a forged region even though it is rotated.