FOCAL: A Forgery Localization Framework based on Video Coding Self-Consistency
| Autor: | Giancarlo Calvagno, Sebastiano Verde, Paolo Bestagini, Simone Milani, Stefano Tubaro, Edoardo Daniele Cannas |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Forgery detection Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION computer.software_genre Convolutional neural network Software Digital evidence Encoding (memory) FOS: Electrical engineering electronic engineering information engineering video forensics Authentication business.industry Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing TK1-9971 Feature (computer vision) Electrical engineering. Electronics. Nuclear engineering Data mining business computer video codecs Coding (social sciences) multimedia forensics |
| Zdroj: | IEEE Open Journal of Signal Processing, Vol 2, Pp 217-229 (2021) |
| DOI: | 10.48550/arxiv.2008.10454 |
| Popis: | Forgery operations on video contents are nowadays within the reach of anyone, thanks to the availability of powerful and user-friendly editing software. Integrity verification and authentication of videos represent a major interest in both journalism (e.g., fake news debunking) and legal environments dealing with digital evidence (e.g., courts of law). While several strategies and different forensics traces have been proposed in recent years, latest solutions aim at increasing the accuracy by combining multiple detectors and features. This paper presents a video forgery localization framework that verifies the self-consistency of coding traces between and within video frames by fusing the information derived from a set of independent feature descriptors. The feature extraction step is carried out by means of an explainable convolutional neural network architecture, specifically designed to look for and classify coding artifacts. The overall framework was validated in two typical forgery scenarios: temporal and spatial splicing. Experimental results show an improvement to the state of the art on temporal splicing localization as well as promising performance in the newly tackled case of spatial splicing, on both synthetic and real-world videos. |
| Databáze: | OpenAIRE |
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