Video Source Characterization Using Encoding and Encapsulation Characteristics
Enes Altinisik, Husrev Taha Sencar, Diram Tabaa

TL;DR
This paper presents a novel camera-model identification method that combines video encoding and encapsulation features, achieving high accuracy and robustness even with partial data, advancing digital forensics.
Contribution
It introduces a joint metadata representation and hierarchical classification approach, improving identification accuracy over traditional methods and addressing missing metadata scenarios.
Findings
Achieves 91% accuracy on 119 classes, outperforming conventional methods by 6.5%.
Demonstrates 57% accuracy with partial video data and missing metadata.
Validates approach on over 20,000 videos from multiple datasets.
Abstract
We introduce a new method for camera-model identification. Our approach combines two independent aspects of video file generation corresponding to video coding and media data encapsulation. To this end, a joint representation of the overall file metadata is developed and used in conjunction with a two-level hierarchical classification method. At the first level, our method groups videos into metaclasses considering several abstractions that represent high-level structural properties of file metadata. This is followed by a more nuanced classification of classes that comprise each metaclass. The method is evaluated on more than 20K videos obtained by combining four public video datasets. Tests show that a balanced accuracy of 91% is achieved in correctly identifying the class of a video among 119 video classes. This corresponds to an improvement of 6.5% over the conventional approach…
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Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Advanced Steganography and Watermarking Techniques
