Next Article in Journal
Ontology-Based Representation for Accessible OpenCourseWare Systems
Previous Article in Journal
Multiple Criteria Decision-Making in Heterogeneous Groups of Management Experts
Article Menu

Export Article

Open AccessArticle
Information 2018, 9(12), 301; https://doi.org/10.3390/info9120301

An Inter-Frame Forgery Detection Algorithm for Surveillance Video

1,2,* , 2,* and 3
1
College of Information Engineering, Ningbo Dahongying University, Ningbo 315175, China
2
CKC Software Laboratory, Ningbo University, Ningbo 315211, China
3
School of Electronics and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
*
Authors to whom correspondence should be addressed.
Received: 16 August 2018 / Revised: 8 November 2018 / Accepted: 20 November 2018 / Published: 28 November 2018
Full-Text   |   PDF [3792 KB, uploaded 28 November 2018]   |  

Abstract

Surveillance systems are ubiquitous in our lives, and surveillance videos are often used as significant evidence for judicial forensics. However, the authenticity of surveillance videos is difficult to guarantee. Ascertaining the authenticity of surveillance video is an urgent problem. Inter-frame forgery is one of the most common ways for video tampering. The forgery will reduce the correlation between adjacent frames at tampering position. Therefore, the correlation can be used to detect tamper operation. The algorithm is composed of feature extraction and abnormal point localization. During feature extraction, we extract the 2-D phase congruency of each frame, since it is a good image characteristic. Then calculate the correlation between the adjacent frames. In the second phase, the abnormal points were detected by using k-means clustering algorithm. The normal and abnormal points were clustered into two categories. Experimental results demonstrate that the scheme has high detection and localization accuracy. View Full-Text
Keywords: surveillance video; video forensics; inter-frame forgery; 2-D phase congruency; k-means clustering surveillance video; video forensics; inter-frame forgery; 2-D phase congruency; k-means clustering
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, Q.; Wang, R.; Xu, D. An Inter-Frame Forgery Detection Algorithm for Surveillance Video. Information 2018, 9, 301.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top