scholarly journals Copy-Move Forgery Detection Using Deep Learning

2021 ◽  
Author(s):  
Jawad Khan
Keyword(s):  
State Of The Art ◽  
Image Editing ◽  
Kernel Pca ◽  
Forgery Detection ◽  
Naked Eye ◽  
Rbf Kernel ◽  
Copy Move Forgery Detection ◽  
Interest Point Detector ◽  
Point Detector

Due to the number of image editing tools available online, image tampering has been easy to execute. The quality of these tools has led these tamperings to steer clear from the naked eye. One such tampering method is called the Copy-Move tampering where a region of the image is copied and pasted elsewhere in the image. We propose a method to deal with this. First, the image is broken to blocks using discrete cosine transform. Next, the dimensionality is reduced using the gaussian RBF kernel PCA. Finally, a new iterative interest point detector is proposed and the image is then sent as input to a CNN that predicts whether the image has been forged or not. The experimental results showed that the algorithm gave an excellent percentage of accuracy, outperforming state of the art methods.

Passive method for rescale detection using quadrature mirror filter based higher order statistical features

2016 ◽  
Vol 14 (05) ◽  
pp. 1650033 ◽  
Author(s):  
Gajanan K. Birajdar ◽  
Vijay H. Mankar
Keyword(s):  
State Of The Art ◽  
Entropy Measure ◽  
Image Editing ◽  
Neural Network Classifier ◽  
Digital Cameras ◽  
Forgery Detection ◽  
Quadrature Mirror Filter ◽  
Detection Algorithms ◽  
Artificial Neural Network Classifier ◽  
Text Component

High resolution digital cameras and state-of-the-art image editing software tools has given rise to large amount of manipulated images leaving no traces of being subjected to any manipulation. Passive or blind forgery detection algorithms are used in order to determine its authenticity. In this paper, an algorithm is proposed that blindly detects global rescaling operation using the statistical models computed based on quadrature mirror filter (QMF) decomposition. Fuzzy entropy measure is employed to choose the relevant features and to remove non-important features whereas artificial neural network classifier is used for forgery detection. Experimental results are presented on grayscale and [Formula: see text]-component images of UCID database to prove the validity of the algorithm under different interpolation schemes. Results are provided for the detection of rescaled images with JPEG compression, arbitrary cropping and white Gaussian noise addition. Further, results are shown using USC-SIPI database to prove the robustness of the algorithm against the type of database.

scholarly journals Copy-Move Forgery Detection in Digital Images using Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1560-1564
Keyword(s):  
Neural Network ◽  
Digital Images ◽  
Experimental Results ◽  
Image Editing ◽  
Forgery Detection ◽  
Software Applications ◽  
Key Points ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
Multiple Stages

Due to easy availability of image editing software applications, many of the digital images are tempered, either to hide some important facts of the image or just to enhance the image. Hence, the integrity of the image is compromised. Thus, in order to preserve the authenticity of an image, it is necessary to develop some algorithms to detect counterfeit parts of an image, if there is any. Two kinds of classic methods exist for the detection of forgery: the key- point based method in which major key points of the image is found and forged part is detected and the block based method that locates the forged part by sectioning the whole image into blocks. Unlike these two classic methods that require multiple stages, our proposed CNN solution provides better image forgery detection. Our experimental results revealed a better forgery detection performance than any other classic approaches.

scholarly journals Novel Copy Move Forgery Detection Based on Repeated Feature Extraction and Delaunay Triangulation

2020 ◽  
Vol 9 (3) ◽  
pp. 2364-2369
Keyword(s):  
Delaunay Triangulation ◽  
Feature Points ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Naked Eye ◽  
Invariant Feature ◽  
Random Sample Consensus ◽  
Feature Transform ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Nowadays new and creative methods of forging images are developed with the invention of sophisticated softwares like Adobe photoshop. Tools available in such softwares will make the forged image look real which cannot be even identified by a naked eye. In this paper, key point based approach of taking out features using Scale Invariant Feature Transform (SIFT) is used. The feature points thus extracted are then modeled to get a set of triangles using Delaunay Triangulation method. These triangles are matched using mean vertex descriptor and the removal of false positives is done using the method of Random Sample Consensus (RANSAC). Implementation show that the proposed approach outdoes the equivalent methods

An Overview on Passive Image Forensics Technology for Automatic Computer Forgery

2016 ◽  
Vol 8 (4) ◽  
pp. 14-25 ◽  
Author(s):  
Jie Zhao ◽  
Qiuzi Wang ◽  
Jichang Guo ◽  
Lin Gao ◽  
Fusheng Yang
Keyword(s):  
State Of The Art ◽  
Image Forensics ◽  
Detection Methods ◽  
Image Editing ◽  
Forgery Detection ◽  
Comprehensive Overview ◽  
Automatic Computer ◽  
Photographic Images ◽  
Detection Technology ◽  
Passive Image Forensics

Currently, with the popularity of sophisticated image editing tools like Photoshop, it is becoming very difficult to discriminate between an authentic image and its manipulated version, which poses a serious social problem of debasing the credibility of photographic images as definite records of events. Passive image forgery detection technology, as one main branch of image forensics, has been regarded as the promising research interest due to its versatility and universality. Automatic computer forgery employs computer intelligent algorithms to forge an image in an automatic way, which is rather more complex than copy-move forgery since the source of duplicated region could be non-continuous. In this paper, the authors provide a comprehensive overview of the state-of-the-art passive detection methods for automatic computer forgery.

scholarly journals A New Copy Move Forgery Detection Method Resistant to Object Removal with Uniform Background Forgery

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Guzin Ulutas ◽  
Gul Muzaffer
Keyword(s):  
Detection Method ◽  
Scale Space ◽  
Source Image ◽  
Image Editing ◽  
Forgery Detection ◽  
Object Removal ◽  
Uniform Background ◽  
Nonlinear Scale Space ◽  
Copy Move Forgery Detection ◽  
Nonlinear Scale

Users transfer large number of images everyday over the Internet. Easy to use commercial and open source image editing tools have made intactness of images questionable. Passive methods have been proposed in the literature to determine authenticity of images. However, a specific type of forgery called “Object Removal with uniform Background forgery” becomes a problem for keypoint based methods in the literature. In this paper, we proposed an effective copy move forgery detection technique. The method uses AKAZE features and nonlinear scale space for detection of copied/pasted regions. The proposed method detects “Object Removal with uniform Background” and “Replication” types of forgeries with high precision compared to similar works. Experimental results also indicate that the method yields better discriminative capability compared to others even if forged image has been rotated, blurred, AWGN added, or compressed by JPEG to hide clues of forgery.

scholarly journals Copy-move forgery detection using convolutional neural network and K-mean clustering

2021 ◽  
Vol 11 (3) ◽  
pp. 2604
Author(s):  
Ava Pourkashani ◽  
Asadollah Shahbahrami ◽  
Alireza Akoushideh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
The Other ◽  
Corner Detection ◽  
Light Illumination ◽  
Forgery Detection ◽  
Post Process ◽  
Copy Move Forgery Detection ◽  
Trained Network

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.

scholarly journals Nonoverlapping Blocks Based Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Sun ◽  
Rongrong Ni ◽  
Yao Zhao
Keyword(s):  
Computational Complexity ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Smooth Part ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
High Computational Complexity

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

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