Abstract
Frame rate up-conversion (FRUC) refers to frame interpolation between adjacent video frames to increase the motion continuity of low frame rate video, which can improve the visual quality on hand-held displays. However, FRUC can also be used for video forgery purposes such as splicing two videos with different frame-rates. We found that most FRUC approaches introduce visual artifacts into texture regions of interpolated frames. Based on this observation, a two-stage blind detection approach is proposed for video FRUC based on the frame-level analysis of average texture variation (ATV). First, the ATV value is computed for each frame to obtain an ATV curve of candidate video. Second, the ATV curve is further processed to highlight its periodic property, which indicates the existence of FRUC operation and further estimates the original frame rate. Thus, the positions of interpolated frames can be inferred as well. Extensive experimental results show that the proposed forensics approach is efficient and effective for the detection of existing typical FRUC approaches such as linear frame averaging and motion-compensated interpolation (MCI). The detection performance is superior to the existing approaches in terms of time efficiency and detection accuracy.
Similar content being viewed by others
References
Bahrami K, Kot AC (2014) A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Signal Process Lett 21(6):751–755
Bestagini P, Battaglia S, Milani S, Tagliasacchi M, Tubaro S (2013) Detection of temporal interpolation in video sequences. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3033–3037
Bian S, Luo W, Huang J (2013) Exposing fake bitrate video and its original bitrate. In: Proceedings of IEEE international conference on image processing (ICIP), pp 4492–4496
Bian S, Luo W, Huang J (2014) Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity. Multimed Tools Appl 72(1):437–451
Bian S, Luo W, Huang J (2014) Exposing fake bit rate videos and estimating original bit rates. IEEE Trans Circ Syst Video Tech 24(12):2144–2154
Dong Q, Yang G, Zhu N (2012) A MCEA based passive forensics scheme for detecting frame-based video tampering. Digit Invest 9(2):151–159
Gironi A, Fontani M, Bianchi T, Piva A, Barni M (2014) A video forensic technique for detecting frame deletion and insertion. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6226–6230
Ha T, Lee S, Kim J (2004) Motion compensated frame interpolation by new block-based motion estimation algorithm. IEEE Trans Consumer Elec 50(2):752–759
Kang SJ, Cho KR, Kim YH (2007) Motion compensated frame rate up-conversion using extended bilateral motion estimation. IEEE Trans Consumer Elec 53(4):1759–1767
Kaufman P (1995) A guide to smarter trading-perry Kaufman on market analysis. Tech Anal Stock Commod 13(6)
Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Info Foren Sec 10(3):507–518
Lin GS, Chang JF (2012) Detection of frame duplication forgery in videos based on spatial and temporal analysis. Inter J Pattern Recog Artif Intell 26(7):1250017
Liu H, Xiong R, Zhao D, Ma S, Gao W (2012) Multiple hypotheses Bayesian frame rate up-conversion by adaptive fusion of motion-compensated interpolations. IEEE Trans Circ Syst Video Tech 22(8):1188–1198
Liu H, Li S, Bian S (2014) Detecting frame deletion in H.264 Video. In: Information security practice and experience, Springer, pp 262–270
Pan ZQ, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcasting 61 (2):166–176
Rocha A, Scheirer W, Boult T, Goldenstein S (2011) Vision of the unseen: current trends and challenges in digital image and video forensics. ACM Comput Surveys 43(4):26–50
Software (2011) Available on http://avisynth.org.ru/mvtools/mvtools2.html. Accessed Dec 2015
Software (2011) Available on http://www.avs4you.com/AVS-Video-Converter.aspx. Accessed Dec 2011
Software (2011) Available on http://compression.ru/video/frame_rate_conversion/index_en_msu.html. Accessed Dec 2015
Software (2011) Available on http://www.imtoo.com/video-converter.html. Accessed Dec 2011
Xia ZH, Wang XH, Sun XM, Liu QS, Xiong NX (2014) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl. doi:10.1007/s11042-014-2381-8
Xia ZH, Wang XH, Sun XM, Wang BW (2014) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Networks 7(8):1283–1291
Xiph.org Video Test Media [derf’s collection]. http://media.xiph.org/video/derf/. Accessed Nov 2014
Wang W, Farid H (2006) Exposing digital forgeries in video by detecting double MPEG compression. In: Proceedings of the 8th workshop on multimedia and security, pp 37–47
Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on multimedia & security, pp 35–42
Wu Y, Jiang X, Sun T, Wang W (2014) Exposing video inter-frame forgery based on velocity field consistency. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2674–2678
YUV Video Sequences. http://trace.eas.asu.edu/yuv/. Accessed Nov 2014
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (61379143, 61232016, 61572183, U1405254), the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20120161110014) and the S&T Program of Xuzhou City (XM13B119) and the PAPD fund. This paper is also supported in part by Southwest University for Nationalities for the Fundamental Research Funds for the Central Universities (82000742). The authors appreciate the nice help from Mr Moses Odero for improving the English usages.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xia, M., Yang, G., Li, L. et al. Detecting video frame rate up-conversion based on frame-level analysis of average texture variation. Multimed Tools Appl 76, 8399–8421 (2017). https://doi.org/10.1007/s11042-016-3468-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3468-1