Summary of Explainable Deepfake Video Detection Using Convolutional Neural Network and Capsulenet, by Gazi Hasin Ishrak et al.
Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet
by Gazi Hasin Ishrak, Zalish Mahmud, MD. Zami Al Zunaed Farabe, Tahera Khanom Tinni, Tanzim Reza, Mohammad Zavid Parvez
First submitted to arxiv on: 19 Apr 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 This paper explores the recent advancements in deepfake technology, which seamlessly inserts individuals into digital media using machine learning and Artificial Intelligence (AI). Deepfakes have become nearly indistinguishable from reality, making them accessible to even novices. However, this accessibility raises security concerns. The primary algorithm used for deepfake creation is GAN (Generative Adversarial Network), which employs machine learning to craft realistic images or videos. This paper aims to develop a model that can differentiate between deepfake-generated frames and originals using CNN (Convolutional Neural Network) and CapsuleNet with LSTM, while also utilizing Explainable AI to foster transparent human-AI relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are fake videos that look real! They’re made using special computer programs called machine learning and Artificial Intelligence. These programs can make the fake videos look just like real ones. Some people use deepfakes for fun, but they can also be used to trick people or spread false information. The researchers in this paper want to make a program that can tell if an image or video is real or made using a deepfake. They’re going to use special techniques called CNN and CapsuleNet with LSTM, and Explainable AI to understand how their program makes decisions. |
Keywords
» Artificial intelligence » Cnn » Gan » Generative adversarial network » Lstm » Machine learning » Neural network
