Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion

Yanli Li, Lala Mei, Ran Li and Changan Wu
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Yanli Li: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Lala Mei: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Ran Li: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Changan Wu: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China

Future Internet, 2018, vol. 10, issue 9, 1-11

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.

Keywords: frame rate up-conversion; frame repetition; video forensics; noise level; periodicity detection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
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