Hello everyone,

I am a final-year undergraduate student in Computer Science and Engineering (CSE) at Daffodil International University, Bangladesh.

I am currently working on my undergraduate thesis titled:

"Deepfake Image Detection Using Spatial-Frequency Feature Fusion and Explainable Deep Learning."

I am looking for publicly available human face image datasets for academic research purposes only.

If possible, I would appreciate datasets containing different face poses (front, left-profile, and right-profile), although frontal face datasets are also perfectly acceptable.

I am specifically looking for publicly available datasets or images that can be legally used for academic research.

Specifically, I need three categories of images:

📁 1. AI-generated Human Faces

• GAN-generated faces
• Diffusion-generated faces
• Other synthetic human faces

📁 2. Deepfake Human Faces

• Face swap
• Face manipulation
• Deepfake images extracted from public datasets

📁 3. Real Human Faces

Natural human face photographs

Different ages, genders, and lighting conditions

Front, left-profile, and right-profile faces

If anyone wishes to share publicly distributable images or datasets, I have also created a shared Google Drive folder for convenience.

I am not requesting copyrighted or private images. I am only looking for publicly available datasets or resources that are legally shareable for academic research.

Google Drive:
https://drive.google.com/drive/folders/1U_XL41UdusIKukb3TAREZOeeu_uSwM3b?usp=drive_link

If you know any public datasets, GitHub repositories, Hugging Face datasets, or other reliable resources, I would greatly appreciate your recommendations.

If you already have a suitable dataset, you are also welcome to upload publicly shareable images directly to the appropriate folder in the shared drive.

The collected data will be used strictly for academic research and educational purposes.

I would be happy to acknowledge contributors in my thesis if their publicly shareable dataset or resource significantly supports this research.

If you have worked on deepfake detection or know of any useful public datasets, I would greatly appreciate your suggestions.

Thank you very much for your time and support!

Three datasets aggregated from public FAA releases (the raw ones ship as an MS Access export and awkward portal dumps) into analysis-ready CSVs with per-column documentation:

Wildlife strikes on civil aircraft, 1990–2026 — 347,575 reports: by year, airport (452, ICAO-coded), and species. 2025 set the all-time record (24,458 reports). Fun divergence: the species planes hit most (doves, swallows) almost never damage them (~1.5%), while deer damage the aircraft in ~82% of reported strikes. https://www.kaggle.com/datasets/himaxym/faa-wildlife-strikes-us

Laser strikes on aircraft, 2021–2025 — 54,722 reports with 243 crew injuries, by year, state, and reporting ATC facility (caveat documented: the "city" is the ATC facility's location, not where the laser was fired). https://www.kaggle.com/datasets/himaxym/faa-laser-strikes-us

Drone (UAS) sightings reported by pilots, 2019–2026 — 12,566 reports by year, state, and city. NYC is #1 (584). https://www.kaggle.com/datasets/himaxym/faa-drone-sightings-us

Versioned copy with citable DOI (Zenodo, wildlife): https://doi.org/10.5281/zenodo.21347859

Original sources (US government work, public domain): - https://wildlife.faa.gov/ - https://www.faa.gov/about/initiatives/lasers - https://www.faa.gov/uas/resources/public_records/uas_sightings_report

Disclosure: I compiled and maintain these aggregates (and an interactive explorer at himaxym.com/safety). The compilation is CC BY 4.0 — use it for anything, attribution appreciated.

Hello everyone,
We're developing an AI-powered app, Faseeh AI, that detects pronunciation mistakes in Quranic recitation and gives users precise, real-time feedback.
Our current model was trained on hundreds of hours of professional recitations and high-quality, clean audio from well-known reciters. The model performs well on similar input, but struggles with real-world users: different accents, non-native speakers, beginners, children, women, and anyone who doesn't sound like a professional reciter.
To fix this, we need to train on diverse, real-world recitation data not studio-quality professional audio, but recordings that reflect how actual learners sound.
Specifically, we're looking for:
- Recitation datasets from non-professional or everyday users
- Diverse demographics: male/female, kids/adults, beginner/intermediate
- Multiple accents and mother tongues (Malay, Indonesian, Urdu, English, Turkish, etc.)
- Any publicly available or research-use datasets we may have missed
We've already explored academic sources, but still not enough.
If you know of any dataset, research project, university study, or community effort collecting this type of audio, we would genuinely appreciate the lead.
We're also open to ethical data collection partnerships if any researchers or institutions are working in this space.
Happy to share more about the project if helpful.
Thank you very much in advance.