Awesome Deepfakes Detection: Curated Index of Papers and Code

Daisy-Zhang curated this repository of research papers, software tools, and source code for deepfake detection, with the latest update recorded in September 2025. It functions as a structured index for the computer vision community to locate academic publications and their corresponding implementations.

Use Cases

• Conducting literature reviews using the categorized paper list
• Implementing baseline models by accessing linked GitHub repositories
• Sourcing specialized tools for video-based deepfake forensic analysis

Strengths

• Updated as of September 2025
• Links academic papers directly to functional source code
• Categorized by image and video detection modalities
• Released under an MIT license for open redistribution

Limitations

• Contains no raw image or video files for direct model training
• Relies on the persistence of external third-party links
• Lacks structured metadata such as citation counts or model accuracy metrics

Provenance

Source

Daisy-Zhang via GitHub

Collection Method

Curated from academic databases and open-source platforms

Freshness

Last updated September 2025.

Geography

Global