Paper Info
Title
METEOR: Measurable Energy Map Toward the Estimation of Resampling Rate via a Convolutional Neural Network
Abstract
AbstractIn recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness.
Year
DOI
Venue
2020
10.1109/TCSVT.2019.2963715
Periodicals
Keywords
DocType
Volume
Image forensics, Machine learning, Tools, Estimation, Parameter estimation, History, Image forensics, resampling, machine learning, convolutional neural network
Journal
30
Issue
ISSN
Citations 
12
1051-8215
1
PageRank 
References 
Authors
0.35
17
4
Name
Order
Citations
PageRank
Feng Ding1368.75
Hanzhou Wu2297.24
Guopu Zhu348227.13
Yun Q. Shi42918199.53