@inproceedings{5aced52c43ac46f8a32ba6acea95e07d,
title = "Run-length and edge statistics based approach for image splicing detection",
abstract = "In this paper, a simple but efficient approach for blind image splicing detection is proposed. Image splicing is a common and fundamental operation used for image forgery. The detection of image splicing is a preliminary but desirable study for image forensics. Passive detection approaches of image splicing are usually regarded as pattern recognition problems based on features which are sensitive to splicing. In the proposed approach, we analyze the discontinuity of image pixel correlation and coherency caused by splicing in terms of image run-length representation and sharp image characteristics. The statistical features extracted from image run-length representation and image edge statistics are used for splicing detection. The support vector machine (SVM) is used as the classifier. Our experimental results demonstrate that the two proposed features outperform existing ones both in detection accuracy and computational complexity.",
keywords = "Characteristic functions, Edge detection, Image splicing, Run-length, Support vector machine (SVM)",
author = "Jing Dong and Wei Wang and Tieniu Tan and Shi, {Yun Q.}",
year = "2009",
doi = "10.1007/978-3-642-04438-0_7",
language = "English (US)",
isbn = "3642044379",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "76--87",
booktitle = "Digital Watermarking - 7th International Workshop, IWDW 2008, Selected Papers",
note = "7th International Workshop on Digital Watermarking, IWDW 2008 ; Conference date: 10-11-2008 Through 12-11-2008",
}