Open Access
ARTICLE
Resampling Factor Estimation via Dual-Stream Convolutional Neural Network
Shangjun Luo1, Junwei Luo1, Wei Lu1,*, Yanmei Fang1, Jinhua Zeng2, Shaopei Shi2, Yue Zhang3,4
1 School of Data and Computer Science, Guangdong Province Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, Guangzhou, 510006, China
2 Academy of Forensic Science, Shanghai, 200063, China
3 College of Information Science and Technology, Jinan University, Guangzhou, 510632, China
4 Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, USA
* Corresponding Author: Wei Lu. Email:
Computers, Materials & Continua 2021, 66(1), 647-657. https://doi.org/10.32604/cmc.2020.012869
Received 15 July 2020; Accepted 28 August 2020; Issue published 30 October 2020
Abstract
The estimation of image resampling factors is an important problem in
image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot
of research interest. However, because of inherent ambiguity, spectrum-based
methods fail to discriminate upscale and downscale operations without any prior
information. In general, the application of resampling leaves detectable traces in
both spatial domain and frequency domain of a resampled image. Firstly, the
resampling process will introduce correlations between neighboring pixels. In this
case, a set of periodic pixels that are correlated to their neighbors can be found in
a resampled image. Secondly, the resampled image has distinct and strong peaks
on spectrum while the spectrum of original image has no clear peaks. Hence, in
this paper, we propose a dual-stream convolutional neural network for image
resampling factors estimation. One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images. The
other is frequency stream that discovers the differences of spectrum between
rescaled and original images. The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and
frequency domain, which is later fed into softmax layer for resampling factor estimation. Experimental results show that the proposed method is effective on
resampling factor estimation and outperforms some CNN-based methods.
Keywords
Cite This Article
S. Luo, J. Luo, W. Lu, Y. Fang, J. Zeng
et al., "Resampling factor estimation via dual-stream convolutional neural network,"
Computers, Materials & Continua, vol. 66, no.1, pp. 647–657, 2021. https://doi.org/10.32604/cmc.2020.012869