教師資料查詢 | 類別: 期刊論文 | 教師: 吳孟倫 MENG-LUEN WU (瀏覽個人網頁)

標題:Image-format-independent Tampered Image Detection Based on Overlapping Concurrent Directional Patterns and Neural Networks
學年
學期
出版(發表)日期2017/03/13
作品名稱Image-format-independent Tampered Image Detection Based on Overlapping Concurrent Directional Patterns and Neural Networks
作品名稱(其他語言)
著者M. L. Wu; C. S. Fahn; Y. F. Chen
單位
出版者
著錄名稱、卷期、頁數Applied Intelligence 47(2), pp. 347-361
摘要With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %.
關鍵字Digital image forensics;Digital image authentication;Tampered image detection;Artificial neural network
語言英文
ISSN0924-669X
期刊性質國外
收錄於SCI;
產學合作
通訊作者M. L. Wu
審稿制度
國別荷蘭
公開徵稿
出版型式,電子版,紙本
相關連結
SDGs
Google+ 推薦功能,讓全世界都能看到您的推薦!