Summary

This repository serves as a comprehensive, curated knowledge base for deepfake detection research, offering an extensive survey of single-modal and multi-modal approaches. It targets researchers, engineers, and practitioners by systematically organizing a vast array of datasets, detection methodologies, adversarial attacks, and defense strategies, providing a structured overview of the field's progress and challenges.

How It Works

The project functions as a meticulously organized repository of information, primarily driven by an associated survey paper. It categorizes and links to numerous research papers, datasets, and technical approaches related to deepfake detection. The content spans from fundamental single-modal visual detection to advanced multi-modal (audio-visual, text-visual) techniques, proactive defense mechanisms, and adversarial attack strategies, facilitating a systematic review of the state-of-the-art.

Quick Start & Requirements

This repository is a curated list of resources and research papers, not a software project with executable code. Therefore, there are no installation instructions, dependencies, or quick-start commands. Users are directed to the cited academic papers and datasets for implementation details and practical application.

Highlighted Details

• Features an extensive catalog of single-modal (GAN-generated, diffusion-generated) and multi-modal (audio-visual, text-visual) deepfake datasets, complete with metadata and download links.
• Provides a systematic taxonomy of detection methodologies, including passive, advanced, proactive, and trustworthy approaches, covering input, model, and learning levels.
• Details a wide range of adversarial attacks (e.g., backdoor, discrepancy minimization) and corresponding defense strategies, with specific sections for GANs and diffusion models.
• Includes links to a regularly updated survey paper on arXiv, offering recent research, systematic taxonomies, and in-depth discussions on deepfake detection progress.

Maintenance & Community

The repository is maintained by the authors of the associated survey paper, with community contributions actively solicited via email or GitHub issues for expanding the resource. While direct community channels like Discord or Slack are not specified, the project encourages collaboration to ensure the completeness and accuracy of its curated information.

Licensing & Compatibility

No specific open-source license is declared for the repository's content itself. As a curated list of research papers and datasets, users must consult the individual licenses and terms of use for each cited work and dataset. Commercial use compatibility is therefore dependent on the licensing of the underlying resources.

Limitations & Caveats

This repository functions purely as a comprehensive survey and resource compilation; it does not offer executable code, pre-trained models, or a direct API for deepfake detection. Users are directed to the cited academic papers and datasets for implementation details, practical application, and further research. The value lies in its organizational structure and breadth of coverage rather than direct tooling.