Introduction to algorithms
Digital Image Processing
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 03
Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
Using noise inconsistencies for blind image forensics
Image and Vision Computing
Design and Implementation of a Knowledge Engineering-Based Dental Diagnostic Expert System (DDES)
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05
An efficient and robust method for detecting copy-move forgery
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Fast copy-move forgery detection
WSEAS Transactions on Signal Processing
Detection of Image Region Duplication Forgery Using Model with Circle Block
MINES '09 Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 01
Determining Image Origin and Integrity Using Sensor Noise
IEEE Transactions on Information Forensics and Security
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The issue of verifying the authenticity and integrity of digital images is becoming increasingly important. Copy-move forgery is one type of image tempering that is commonly used for manipulating digital content; in this case, some part of an image is copied and pasted on another region of the image. Using a non-intrusive approach to solve this problem is becoming attractive because it does not need any embedded information, but it is still far from being satisfactory. In this paper, an efficient non-intrusive method for copy-move forgery detection is presented. The method is based on image segmentation and a new denoising algorithm. First, the image is segmented using a multi-scale segmentation algorithm. Then, using the noise pattern of each segment, a separate noise image is created. The noise images are used to estimate the overall noise of the image which is further used to re-estimate the noise pattern of different segments. The image segments with similar noise histograms are detected as tampered. A comparison with a state-of-the art non-intrusive algorithm shows that the proposed method performs better.