A Feature Fusion Model with Rank-Sparsity Decomposition for Image Tampering Localization

• Main Author: 劉品均
• Other Authors: 許秋婷
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/67165329995201795201

碩士 === 國立清華大學 === 資訊工程學系 === 102 === Nowadays, image editing softwares are powerful and user-friendly that most people can easily create visual-pleasant tampered images. The techniques of image forensics have been developed for about two decades. However, most techniques only focus on one tampering trace. In addition, they sometimes assume that the suspicious region is known a priori. The purpose of this work is to develop a feature fusion model which can utilize all the available traces and automatically localize the tampered region. We adopt the early fusion scheme to fuse features in order to consider all the available features simultaneously. We propose to utilize Robust Principal Component Analysis (RPCA) to decompose one test image into authentic parts and tampered parts. We assume the authentic parts share similar feature behaviors, i.e., low-rank, and the tampered parts are sparse and also share similar feature behaviors, i.e., sparse and low-rank. We consider the spatial consistency of the detected tampered parts by using Group-Sparsity. The experimental results demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art methods in both synthetic and realistic cases.