Frame-Deletion Detection for Static-Background Video Based on Multi-scale Mutual Information

Zhao, Yanjia; Pang, Tianming; Liang, Xiaoyun; Li, Zhaohong

doi:10.1007/978-3-319-68542-7_31

Cited by 8 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As its name means, this method discovered the inconsistency trace by measuring the structure similarity between adjacent frames. Moreover, Zhao and Pang [12] exploited the mutual information between adjacent frames to quantify the content similarity. Since two originally non-adjacent frames were re-adjacent after FD, at this point, inconsistency would occur as a result of FD.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is natural to consider the extraction of the moving objects and estimate the optical flows for them. In recent years, Robust Principal Component Analysis (RPCA) [19] [20] [21] (12) where  denotes the nuclear norm, and 1 denotes the sum of the absolute values of the matrix.…”

Section: A Moving Objects Extraction and Its Optical Flow Estimationmentioning

confidence: 99%

“…The optical flow estimation is a classic approach to calculate distribution of the motion vectors in image sequence. In this paper, we apply the Horn-Schunck technique [22] (13) According to the framework of Euler-Lagrange equation, equation (12) has the solution to assign each pixel in t F a velocity.…”

Section: A Moving Objects Extraction and Its Optical Flow Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Frame Deletion Detection Based on Optical Flow Orientation Variation

Huo

2021

IEEE Access

View full text Add to dashboard Cite

Frame deletion detection is an arrestive field for video forensics in recent years. This paper presents a frame deletion detection algorithm based on optical flow orientation variation. Optical flow field has been proved to be effective for the detection of the frame deletion operation. However, current opticalflow based methods have relatively poor detection performance for real-world videos. Our research aims to develop a new forensic method to detect frame deletion for real-world videos. The proposed algorithm firstly develops an analytical model for flow orientation variation between adjacent optical flow fields, and exhibits a novel forensic clue for frame deletion detection, under an ideal but reasonable assumption. Accordingly, an effective descriptor, namely pseudo flow orientation variation (PFOV), is created to approximate the flow orientation variation, as a discriminative description feature to capture the frame deletion trace. The Frobenius norm and a smooth function are also introduced to quantify each descriptor to form an one-dimensional time series, and further statistics technique is adopted to detect the anomalous points in the time series caused by frame deletion. In addition, to improve the descriptor performance, the Robust Principal Component Analysis (RPCA) is applied to extract moving objects from video sequences and compute the proposed descriptor on them. The tests are made on 324 real-world videos, and experiment results demonstrate that the true positive rate can reach 90.12%, meanwhile the false alarm rate is 7.71%, which indicates the superiority of the proposed method in the forensic algorithms for real-world video.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Moving Objects Extraction and Its Optical Flow Estimationmentioning

confidence: 99%

Section: A Moving Objects Extraction and Its Optical Flow Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Frame Deletion Detection Based on Optical Flow Orientation Variation

Huo

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Finally, they extracted time dimension matrix to calculate correlation to determine whether there is a frame insertion or deletion forgery. Zhao et al [26] proposed an algorithm to detect the frame-deleting forgery. The feature extraction based on the normalized mutual information feature, and make use of generalized ESD test to localize the tampering point.…”

Section: Introductionmentioning

confidence: 99%

An Inter-Frame Forgery Detection Algorithm for Surveillance Video

Wang

2018

Information

View full text Add to dashboard Cite

Surveillance systems are ubiquitous in our lives, and surveillance videos are often used as significant evidence for judicial forensics. However, the authenticity of surveillance videos is difficult to guarantee. Ascertaining the authenticity of surveillance video is an urgent problem. Inter-frame forgery is one of the most common ways for video tampering. The forgery will reduce the correlation between adjacent frames at tampering position. Therefore, the correlation can be used to detect tamper operation. The algorithm is composed of feature extraction and abnormal point localization. During feature extraction, we extract the 2-D phase congruency of each frame, since it is a good image characteristic. Then calculate the correlation between the adjacent frames. In the second phase, the abnormal points were detected by using k-means clustering algorithm. The normal and abnormal points were clustered into two categories. Experimental results demonstrate that the scheme has high detection and localization accuracy.

show abstract