Paper Info
Title
Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion.
Abstract
Frame repetition (FR) is a common temporal-domain tampering operator, which is often used to increase the frame rate of video sequences. Existing methods detect FR forgery by analyzing residual variation or similarity between video frames; however, these methods are easily interfered with by noise, affecting the stability of detection performance. This paper proposes a noise-level based detection method which detects the varying noise level over time to determine whether the video is forged by FR. Wavelet coefficients are first computed for each video frame, and median absolute deviation (MAD) of wavelet coefficients is used to estimate the standard deviation of Gaussian noise mixed in each video frame. Then, fast Fourier transform (FFT) is used to calculate the amplitude spectrum of the standard deviation curve of the video sequence, and to provide the peak-mean ratio (PMR) of the amplitude spectrum. Finally, according to the PMR obtained, a hard threshold decision is taken to determine whether the standard deviation bears periodicity in the temporal domain, in which way FR forgery can be automatically identified. The experimental results show that the proposed method ensures a large PMR for the forged video, and presents a better detection performance when compared with the existing detection methods.
Year
DOI
Venue
2018
10.3390/fi10090084
FUTURE INTERNET
Keywords
Field
DocType
frame rate up-conversion,frame repetition,video forensics,noise level,periodicity detection
Residual,Pattern recognition,Computer science,Computer network,Median absolute deviation,Fast Fourier transform,Frame rate,Operator (computer programming),Artificial intelligence,Gaussian noise,Standard deviation,Wavelet
Journal
Volume
Issue
ISSN
10
9
1999-5903
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yanli Li185.51
Lala Mei200.34
Ran Li3306.80
Chang-an Wu4195.66