Summary of A Multimodal Framework For Deepfake Detection, by Kashish Gandhi et al.
A Multimodal Framework for Deepfake Detection
by Kashish Gandhi, Prutha Kulkarni, Taran Shah, Piyush Chaudhari, Meera Narvekar, Kranti Ghag
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of deepfakes, AI-generated synthetic media that can deceive reality. The authors propose a comprehensive approach to detect deepfakes by combining visual and auditory analysis. They develop models for facial feature extraction and mel-spectrogram analysis, which are then applied using machine learning and deep learning techniques. To test their approach, they swap real and deepfake audio in the dataset and classify samples as deepfake if either component is identified as such. The proposed multimodal framework achieves an accuracy of 94%, highlighting its potential to mitigate the risks associated with deepfakes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are a type of AI-generated fake media that can be very convincing. This technology has the power to deceive people and create misinformation, which can have serious consequences for our privacy and security. To combat this issue, researchers are working on ways to detect deepfakes. One approach is to analyze both the visual and auditory elements of a video or audio clip. The authors of this paper developed models that can do just that, by looking at facial features and audio patterns. They tested their approach using real and fake audio clips and found it was very effective in identifying deepfakes. |
Keywords
* Artificial intelligence * Deep learning * Feature extraction * Machine learning
