Summary of Media Forensics and Deepfake Systematic Survey, by Nadeem Jabbar Ch et al.
Media Forensics and Deepfake Systematic Survey
by Nadeem Jabbar CH, Aqib Saghir, Ayaz Ahmad Meer, Salman Ahmad Sahi, Bilal Hassan, Siddiqui Muhammad Yasir
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 paper explores various methods for creating and manipulating deepfakes, which are highly realistic facial features that can be used to alter identities, expressions, or attributes. The authors explain different approaches to making deepfakes, including attribute manipulation, expression swap, entire face synthesis, and identity swap. They also discuss recent developments in deepfake detection models using deep learning techniques. The paper presents a comprehensive review of methods for manipulating facial images and detecting altered images, highlighting significant benchmarks, public databases, and the outcomes of technical evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are very realistic fake faces that can be used to make movies look better or spread false information. In this paper, researchers explain many ways to create deepfakes, including changing identities, expressions, or attributes. They also show how to detect if an image is real or fake using a special model. The authors discuss the latest developments in making and detecting deepfakes, which can help speed up the development of new imaging tools. |
Keywords
* Artificial intelligence * Deep learning
