Paper Info
Title
Mantra-Net: Manipulation Tracing Network For Detection And Localization Of Image Forgeries With Anomalous Features
Abstract
To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net. Unlike many existing solutions, ManTra-Net is an end-to-end network that performs both detection and localization without extra preprocessing and postprocessing. ManTra-Net is a fully convolutional network and handles images of arbitrary sizes and many known forgery types such splicing, copy-move, removal, enhancement, and even unknown types. This paper has three salient contributions. We design a simple yet effective self-supervised learning task to learn robust image manipulation traces from classifying 385 image manipulation types. Further, we formulate the forgery localization problem as a local anomaly detection problem, design a Z-score feature to capture local anomaly, and propose a novel long short-term memory solution to assess local anomalies. Finally, we carefully conduct ablation experiments to systematically optimize the proposed network design. Our extensive experimental results demonstrate the generalizability, robustness and superiority of ManTra-Net, not only in single types of manipulations/forgeries, but also in their complicated combinations.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00977
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Computer science,Artificial intelligence,Mantra,Tracing
Conference
1063-6919
Citations 
PageRank 
References 
5
0.41
0
Authors
3
Name
Order
Citations
PageRank
Yue Wu133131.69
Wael Abd-Almageed224824.52
Premkumar Natarajan387479.46