Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 23-29.doi: 10.6040/j.issn.1672-3961.0.2018.190

• Machine Learning & Data Mining • Previous Articles     Next Articles

Detecting frame of repetition forgery based on noise level estimation

Lala MEI(),Ran LI*(),Chang'an WU   

  1. School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, Henan, China
  • Received:2018-05-31 Online:2019-02-20 Published:2019-03-01
  • Contact: Ran LI E-mail:mll2106@163.com;liran358@163.com
  • Supported by:
    信阳师范学院研究生科研创新基金(2017KYJJ47)

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Abstract:

Detecting method of the varying noise level in temporal-domain was investigated based on noise-level, which could identify frame repetition (FR) forgery. Wavelet coefficients were computed for each video frame, and median absolute deviation (MAD) of wavelet coefficients was used to estimate the standard deviation of Gaussian noise mixed in each video frame. Fast Fourier transform (FFT) was 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. In order to automatically identify FR forgery, a hard threshold decision based on PMR was taken to determine whether the standard deviation had a periodicity in time domain. The experimental results showed that the proposed method ensured a large PMR for the forged video and high detection accuracy. The proposed method presented a better detection performance when compared with the existing detection, avoiding the performance loss from noise.

Key words: frame rate up-conversion, frame repetition, video forensics, noise level, periodicity detection

CLC Number: 

  • TN919.8

Fig.1

Illustration of FR forgery"

Fig.2

Flow chart of noise level detection"

Fig.3

Illustrations of standard-deviation curve for both original and forged Foreman sequences"

Fig.4

Illustrations of Fourier spectrum for both original andforged Foreman sequences"

Fig.5

Illustrations of Fourier spectrums for both original and forged video sequences"

Table 1

Average PMR values of amplitude spectrums for different detection methods"

检测方法 QCIF CIF 720P 1080P 整体测试集
NS PS Δ NS PS Δ NS PS Δ NS PS Δ NS PS Δ
残差检测 6.76 17.24 0.61 7.67 19.88 0.61 10.04 29.58 0.66 11.73 23.88 0.51 8.63 21.98 0.61
相似性检测 7.36 21.91 0.66 7.63 24.31 0.69 10.17 29.63 0.66 11.90 26.30 0.55 8.79 25.23 0.65
噪声水平检测 2.42 27.32 0.91 2.45 30.17 0.92 2.93 28.32 0.89 2.92 27.95 0.889 2.62 28.85 0.91

Table 2

FNR, FPR and DA of different detection methods"

检测方法 QCIF CIF 720P 1080P 整体测试集
FNR FPR DA FNR FPR DA FNR FPR DA FNR FPR DA FNR FPR DA
残差检测 0.70 0.10 0.60 0.71 0 0.64 0.90 0 0.55 1.00 0 0.50 0.80 0.02 0.59
相似性检测 0.70 0 0.65 0.67 0 0.67 0.80 0 0.60 1.00 0 0.50 0.76 0 0.62
噪声水平检测 0 0 1.00 0 0 1.00 0 0 1.00 0.25 0 0.88 0.04 0 0.98
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