Next Article in Journal
Rainfall Runoff Analysis and Sustainable Soil Bed Optimization Engineering Process: Application of an Advanced Decision-Making Technique
Previous Article in Journal
Vector-Coupled Flight Controller Design Based on Multivariable Backstepping Sliding Mode
Open AccessArticle

Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

1
College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
2
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1223; https://doi.org/10.3390/sym11101223
Received: 14 August 2019 / Revised: 17 September 2019 / Accepted: 19 September 2019 / Published: 1 October 2019
Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms. View Full-Text
Keywords: image manipulation detection; Faster R-CNN; edge detection; max pooling image manipulation detection; Faster R-CNN; edge detection; max pooling
Show Figures

Figure 1

MDPI and ACS Style

Wei, X.; Wu, Y.; Dong, F.; Zhang, J.; Sun, S. Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN. Symmetry 2019, 11, 1223.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop