Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion

LI Ran; MEI Lala; WU Chang'an; ZHU Xiuchang

doi:10.11999/JEIT170502

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 713-720

LI Ran, MEI Lala, WU Chang'an, ZHU Xiuchang. Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion[J]. Journal of Electronics & Information Technology, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502

Citation:

LI Ran, MEI Lala, WU Chang'an, ZHU Xiuchang. Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion[J]. Journal of Electronics & Information Technology, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502

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Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion

doi: 10.11999/JEIT170502

1.
2.
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
3.
(College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Funds:

The National Natural Science Foundation of China (61501393)

Received Date: 2017-05-24
Rev Recd Date: 2017-11-10
Publish Date: 2018-03-19

Abstract

Abstract

Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is one of the common temporal-domain tampering methods of video. The existing methods recognize MC-FRUC tampering by passively analyzing statistical characteristics of video; however, the non-stationarity in statistics of video affects the stability of forensics. This paper proposes an active noise-mixed forensics algorithm. First, white Gaussian noises are produced using a pseudorandom sequence, and these noises are added into the original video sequence. Second, based on the median absolute deviation of wavelet coefficients, the standard deviation of mixed Gaussian noises in each video frame is estimated. Last, the periodicity of standard deviation varying in time domain is detected, and MC-FRUC tampering with a hard-thresholding operation is automatically identified. Experimental results indicate that the proposed algorithm presents better performance of forensics for various MC-FRUC methods, and can still ensure high detection accuracy especially after videos are denoised or compressed.
- Motion-Compensated Frame Rate Up-Conversion (MC-FRUC),
- Active forensics,
- Gaussian noise,
- Median absolute deviation,
- Periodicity detection

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References(16)

References

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