# 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.  
frame rate up-conversion; frame repetition; video forensics; noise level; periodicity detection 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/610090084; 10.3390/61009084; 10.  
that through the periodicity of the structural similarity (SSIM) index [7]. [PERSON] et al. [8] found the periodicity of inter-frame similarity by computing the distance between features of adjacent frames. Moreover, some existing works also used the bad effects in the interpolated frames to trace FR forgery, e.g., [PERSON] et al. [9] explored the edge intensity to measure evidence of tampering; [PERSON] et al. [10] found the relationship between statistics of residual and interpolation to reveal tampering; [PERSON] et al. [11] used the artifact-indicated map and tchebichef moments to identify the frame interpolation. The aforementioned methods avoid measuring the pixel residual, so they can reduce the bad effects of lossy compression. However, the inter-frame similarity is easily influenced by noise. Once the forged video is polluted by some noise, similarity detection suffers some performance degradation. Therefore, an FR forensic technique, which is robust to noise, is required.  
Due to thermal noise in cameras and natural noise from the outside, noises always exist in video sequences. While the video is forged by FR, the statistics of noise in video will change. Therefore, by detecting the noise varying in the temporal domain, it can be determined whether the video is forged by FR. The objective of this paper is to use Gaussian distribution to model the noise in a video frame, and find the FR forgery by observing the temporal variation of noise level. The proposed method directly uses the noise statistics to detect fake traces, avoiding the performance loss from noise. The main contributions of this work can be summarized as follows:  
* This paper uses Gaussian distribution to model the noise in a video frame, and find the FR forgery by observing the temporal variation of the noise level.
* The proposed method directly uses the noise statistics to detect fake traces, avoiding the performance loss from noise.  
Experimental results indicate that the proposed noise-level detection method can effectively identify the up-converted video sequences by FR, and guarantee high detection accuracy.  
The rest of this paper is organized as follows. Section 2 briefly reviews residual detection and similarity detection. Section 3 describes the proposed noise-level detection method in detail. Section 4 presents the experimental results. Finally, Section 5 concludes this paper.