This repository is a comprehensive survey and curated collection of research papers on deepfake generation and detection, focusing on facial manipulation. It serves as a valuable resource for researchers, engineers, and practitioners in computer vision and AI security interested in understanding and advancing the state-of-the-art in creating and identifying synthetic media.

How It Works

The survey categorizes deepfake research into key areas: Face Swapping, Face Reenactment, Talking Face Generation, Facial Attribute Editing, and Forgery Detection. It also includes related domains like Face Super-resolution, Portrait Style Transfer, Body Animation, and Makeup Transfer. Each category lists relevant papers with publication venue, year, and links to code repositories where available, providing a structured overview of the field.

Quick Start & Requirements

This repository is a curated list of research papers and does not have a direct installation or execution command. It requires no specific software to "run" but serves as a reference guide.

Highlighted Details

• Comprehensive Coverage: Encompasses a wide range of deepfake generation and detection techniques, including emerging areas like diffusion models and 3D-aware synthesis.
• Code Availability: Prioritizes papers with publicly available code, facilitating reproducibility and further research.
• Up-to-date: Includes recent publications from major conferences and journals, reflecting the rapid advancements in the field.
• Structured Organization: Clearly categorizes research, making it easy to navigate and find relevant papers.

Maintenance & Community

The repository is maintained by the authors of the survey paper. Contributions and suggestions for missing work are welcomed via pull requests or direct contact.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT or Apache), but the licensing of the individual papers and code repositories linked within it will vary. Users should consult the licenses of the respective linked resources.

Limitations & Caveats

As a survey, this repository lists research papers and does not provide implementations or tools. The effectiveness and applicability of the cited methods depend on the specific implementation and dataset used in each paper.