期刊論文
| 學年 | 105 |
|---|---|
| 學期 | 2 |
| 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 | en |
| ISSN | 0924-669X |
| 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 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120008 ) |