Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

Digital video tampering detection: An overview of passive techniques

“…Fixed Frame repetition and spatiotemporal region duplication Residual computed between adjacent frames, crosscorrelation of residual (Bestagini et al, 2013b) Detection accuracy decreases with increase in compression Frame interpolation MVs, periodicity of squared prediction error (Bestagini et al, 2013a) Compression affects performance; fails to detect downsampling where interpolation factor >= 2 Frame insertion, deletion and duplication Optical flow (Wang et al, 2014b) Frame deletion accuracy is less; complicated backgrounds, frequent motions and compression affects performance Zernike Opponent chromaticity moments (ZOCM) (Liu and Huang, 2015) Good performance on camera in stationary or slow-moving mode; fails in dynamic background videos Frame repetition and deletion Motion energy at spatial region of interest (SROI), average object area and entropy (Gupta et al, 2015) Works well on videos with high motion content (Su et al, 2009) used MCEA for frame deletion detection, which is a side effect of the blocking impairment and motion-compensated prediction. This appears in video codecs where block-based motion-compensated prediction is used.…”

“…ity space by (Liu and Huang, 2015) for detection of frame insertion, deletion, replacement, and duplication forgeries. This gives the chromaticity aberration of the frame.…”

Hybrid Image-Retrieval Method for Image-Splicing Validation

“…Zernike moments have a wide range of applications in different fields such as image recognition and classification [20,25,27,28], copy-move-forgery detection [29][30][31][32][33], video-forgery detection [34], watermark detection [35][36][37], and medical-image retrieval [38]. In the copy-move forgery-detection problem, Zernike moment features-based methods notably showed impressive performances to different kinds of transformations in comparison with other approaches [29][30][31].…”

Authentication of Surveillance Videos: Detecting Frame Duplication Based on Residual Frame

“…In fact, most videos do not contain any digital watermark or fingerprint. As an alternative, several passive techniques (do not depend on prior information about a digital video) have been proposed during the past decade .…”

“…In fact, most videos do not contain any digital watermark or fingerprint. As an alternative, several passive techniques (do not depend on prior information about a digital video) have been proposed during the past decade (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Wang and Farid (11) used the spatial and temporal correlations among sequential video frames.…”