Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity

Bian, Shan; Luo, Weiqi; Huang, Jiwu

doi:10.1007/s11042-013-1364-5

Cited by 23 publications

(16 citation statements)

References 16 publications

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“…In the proposed FR forensic method, the threshold Thr is only a parameter, and it is important for the accuracy of detection. Based on the PMR distribution on the testing video database, the cross-validation proposed by [6] is used to select a proper threshold. According to the experimental results in Section 4.2, Thr is set to be 5.…”

Section: Periodicity Detectionmentioning

confidence: 99%

“…Compared with residual detection, similarity detection can reduce the performance loss from lossy compression, and its innovation lies in finding a FR forgery by measuring the structural similarity between adjacent frames. Bian et al [6] achieved that through the periodicity of the structural similarity (SSIM) index [7]. Yang et al [8] found the periodicity of inter-frame similarity by computing the distance between features of adjacent frames.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Mei²,

Li³

et al. 2018

Future Internet

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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.

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Section: Periodicity Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Mei²,

Li³

et al. 2018

Future Internet

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“…Although this algorithm was not designed to detect forgeries, it could still serve as a utilitarian preprocessing step that could provide useful hints regarding content authenticity. This algorithm was an extension and improvement of the previous algorithms proposed by the same authors in [119,120]. While the technique in [119] was evaluated on MPEG-2 encoded videos only, in [120], the authors worked on detecting up-converted videos only, without actually determining the original bitrates of these up-converted videos.…”

Section: Video Bitrate Up-conversion Detectionmentioning

confidence: 99%

Video content authentication techniques: a comprehensive survey

Singh

Aggarwal

2017

Multimedia Systems

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“…Up to now, just a few works have been reported about this forensic issue. To detect the original frame-rate of fake quality videos, Bian et al [21] proposed an effective method by analyzing the periodic properties of inter-frame similarity. Chen and Shi [12] proposed a method based on Benford's law to detect whether an MPEG-2 video is recompressed from a lower/higher bit rate one, and then Sun et al [13] proposed an improved method based on [12].…”

Section: Introductionmentioning

confidence: 99%

Exposing Fake Bit Rate Videos and Estimating Original Bit Rates

Bian

Luo

Huang

2014

IEEE Trans. Circuits Syst. Video Technol.

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Bit rate is one of the important criterions for digital video quality. With some video tools, however, video bit rate can be easily increased without improving the video quality at all. In such a case, a claimed high bit rate video would actually have poor visual quality if it is up-converted from an original lower bit rate version. Therefore, exposing fake bit rate videos becomes an important issue for digital video forensics. To the best of our knowledge, although some methods have been proposed for exposing fake bit rate MPEG-2 videos, no relative work has been reported to further estimate their original bit rates. In this paper, we first analyze the statistical artifacts of these fake bit rate videos, including the requantization artifacts based on the first-digit law in the DCT frequency domain (12-D) and the changes of the structural similarity indexes between the query video and its sequential bit rate down-converted versions in the spatial domain (4-D), and then we propose a compact yet very effective 16-D feature vector for exposing fake bit rate videos and further estimating their original bit rates. The extensive experiments evaluated on hundreds of video sequences with four different resolutions and two typical compression schemes (i.e., MPEG-2 and H.264/AVC) have shown the effectiveness of the proposed method compared with the existing relative ones.Index Terms-Digital video forensics, video bit rate estimation, video quality forgery.

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Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity

Cited by 23 publications

References 16 publications

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

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

Video content authentication techniques: a comprehensive survey

Exposing Fake Bit Rate Videos and Estimating Original Bit Rates

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