Summary of Deepfake Detection and the Impact Of Limited Computing Capabilities, by Paloma Cantero-arjona et al.
Deepfake Detection and the Impact of Limited Computing Capabilities
by Paloma Cantero-Arjona, Alfonso Sánchez-Macián
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 paper proposes a novel approach to detecting deepfakes, a sophisticated video manipulation technique, using artificial intelligence models that can operate efficiently even with limited computing resources. The researchers aim to analyze the applicability of various deep learning techniques on existing datasets and explore ways to improve their performance under resource-constrained scenarios. The goal is to develop a generic detection system for forged videos, ensuring the accuracy of information and preventing misinformation and mass manipulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect fake videos better using artificial intelligence. Right now, there’s a big problem with deepfakes – they’re hard to spot! This research aims to find ways to identify these fake videos even when we don’t have lots of computer power. The goal is to make sure information is accurate and people can’t manipulate others by making fake videos look real. |
Keywords
* Artificial intelligence * Deep learning
