針對(duì)視頻運(yùn)動(dòng)補(bǔ)償幀率提升篡改的主動(dòng)混噪取證算法
doi: 10.11999/JEIT170502
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2.
(信陽師范學(xué)院計(jì)算機(jī)與信息技術(shù)學(xué)院 信陽 464000)
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3.
(南京郵電大學(xué)通信與信息工程學(xué)院 南京 210003)
基金項(xiàng)目:
國家自然科學(xué)基金(61501393)
Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion
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2.
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
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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)
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摘要: 運(yùn)動(dòng)補(bǔ)償幀率提升(MC-FRUC)是常見的視頻時(shí)域篡改手段。現(xiàn)有方法依靠被動(dòng)分析視頻統(tǒng)計(jì)特征發(fā)現(xiàn)MC-FRUC篡改，然而，視頻統(tǒng)計(jì)特性的非平穩(wěn)性影響了取證性能的穩(wěn)定性。該文提出一種主動(dòng)混噪取證算法，通過預(yù)先混入統(tǒng)計(jì)特性已知的高斯白噪聲，提高M(jìn)C-FRUC取證的準(zhǔn)確度。首先，利用偽隨機(jī)序列生成高斯白噪聲，加入原始視頻序列。接著，由小波系數(shù)的絕對(duì)中位差預(yù)測(cè)各視頻幀中混入高斯噪聲的標(biāo)準(zhǔn)差。最后，檢測(cè)高斯噪聲標(biāo)準(zhǔn)差的時(shí)域變化周期性，通過硬閾值判決，自動(dòng)甄別MC-FRUC篡改。實(shí)驗(yàn)結(jié)果表明，針對(duì)不同的MC-FRUC偽造方法，提出算法均表現(xiàn)出良好的取證性能，尤其是當(dāng)采用去噪、壓縮等操作后處理視頻后，提出算法仍能確保較高的檢測(cè)準(zhǔn)確度。
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關(guān)鍵詞:
- 運(yùn)動(dòng)補(bǔ)償幀率提升 /
- 主動(dòng)取證 /
- 高斯噪聲 /
- 絕對(duì)中位差 /
- 周期性檢測(cè)
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. -
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