Sotto AI – Deepfake Detection System
📌 Developed by: Junaid Kabir (Lucifer) 🌐 Hosted at: https://sottoai.vercel.app 📄 License: Proprietary, Copyright © 2026 Junaid Kabir. All rights reserved.
🚀 Overview
Sotto AI (“Sotto” is the project name, inspired by the idea of uncovering reality) is an advanced multi-asset deepfake detection system designed to provide almost real-time analysis of images and videos. Built with an ensemble of specialized AI models, it delivers robust, transparent, and highly-accurate results using forensic-oriented techniques.
- Multi-Model Judging System: Routes the input through several expert models (visual, metadata), who “vote” using confidence scores.
- Heatmap Visualizations: Highlights manipulated regions in media files for easy inspection.
- Cross-Modality Support: Detects deepfakes in images and videos using modality-specific pipelines.
📌 Key Features
| Feature | Description |
|---|---|
| Multi-Judge Decision Engine | Ensemble of Visual, Metadata, and ChiefJustice AIs that vote to maximize accuracy |
| Visual Heatmaps | Overlay pinpointing image/video manipulation artifacts |
| Metadata Forensics | Analyzes available metadata for signs of editing, creation timestamps, and device information |
| Real-Time Feedback | Rapid processing suitable for integration with web apps and APIs |
🛠️ How It Works
- Upload media (image/video) via API/UI.
- Visual pipeline analyzes compression artifacts, edge inconsistencies, lighting, synthetic-image artifacts and generation-model fingerprints.
- Metadata analyzer checks file integrity and origin.
- ChiefJustice AI aggregates the expert scores, producing a final verdict with overall confidence.
Note: Audio analysis capabilities are currently under development and will be released soon.
🧠 Technical Specifications
Architecture: Ensemble of specialized visual models + metadata module + aggregation layer.
Accuracy: 95.7% on [dataset], under [evaluation protocol].
Latency: Sub-second per image; a few seconds per 5-minute video on standard cloud GPU.
Report Output: JSON, including:
- Media metadata
- Multi-model confidence
- Heatmap overlay images
⚠️ Limitations
- May underperform on extremely low-resolution or heavily compressed media.
- Detection accuracy depends on known synthetic-media generation techniques; novel or unknown generative methods may reduce efficacy.
- Provenance trail relies on embedded metadata; stripped/altered metadata may hinder analysis.
🤝 Use Cases & Ideal Users
- Journalists & Newsrooms – Validate coming-in user content before publishing.
- Law Enforcement & Forensics – Support legal workflows with detailed, evidence-oriented forensic reports.
- Enterprises & Cybersecurity – Auto-screen media for internal communications or executive content.
- Government Agencies – Monitor political/social media content during sensitive events or elections.
📄 Licensing & Usage
This model and its components are proprietary. Distribution, modification, or reverse-engineering is strictly prohibited. Unauthorized copying, redistribution, modification, or commercial use may result in enforcement actions, including copyright takedown requests where applicable. For commercial or enterprise licensing, contact:
🔗 Additional Resources
- Full Documentation & API reference: https://github.com/mrlucifer404/SOTTO-AI
- Research references on deepfake evaluation (e.g. "Deepfake-Eval-2024" benchmark)
🧾 Citation
If you use Sotto AI in research or publications, please cite:
Sotto AI: Multi-Model Deepfake Detection System. Lucifer. Version 1.0, released December 22, 2025.
👨💻 About Junaid Kabir
Founder of the Sotto AI initiative, Junaid has pioneered practical, ensemble-based detection systems in deep forensics. Sotto AI reflects his commitment to tech-driven truth and innovative defense against digital deception.
“Truth always leaves a trace. Sotto finds it.”
Disclaimer: This README is tailored for Hugging Face hosting. Adjust links, dependencies, or metadata to align with deployment specifics.