Summary of Camera Model Identification Using Audio and Visual Content From Videos, by Ioannis Tsingalis et al.
Camera Model Identification Using Audio and Visual Content from Videos
by Ioannis Tsingalis, Christos Korgialas, Constantine Kotropoulos
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework identifies devices using audio, visual content, or their fusion, with a focus on multimedia forensic applications. Leveraging Convolutional Neural Networks, the device identification problem is tackled as a classification task. The experimental evaluation demonstrates promising performance when using individual modalities, and although fusion results don’t consistently surpass them, they show potential for enhancing classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to identify devices has been developed by combining audio and visual information from devices. This helps with tasks like solving crimes or identifying fake videos. The system uses special computer programs called Convolutional Neural Networks to sort devices into different categories. When tested, the system worked well using just one type of information (audio or video), but when combining both, it showed some promise for improving its performance. |
Keywords
* Artificial intelligence * Classification
