A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise

Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is c...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 12; no. 6; p. 1362
Main Authors: Li, Qian, Wang, Rangding, Xu, Dawen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2023
Subjects:
Accuracy
Algorithms
Cameras
Clustering
Copying
Deep learning
Digital cameras
Digital video
Forensic sciences
Forgery
Localization
Mathematical analysis
Methods
Moving object recognition
Semantics
Sensors
Signal processing
Splicing
China
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.
Bibliography:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12061362