title: Scam.AI
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Scam.AI
Detection systems for AI-driven fraud — deepfakes, document forgery, synthetic media, and adversarial attacks against identity verification.
What We Do
Scam.AI builds detection systems that protect identity-verification pipelines, financial-document workflows, and digital media ecosystems from the next generation of AI-driven fraud. Our research portfolio spans deepfake detection, document forgery forensics, AI-generated image attribution, age-estimation robustness, and behavioral-biometric verification — published at top venues (CVPR, arXiv) and released here as open benchmarks for the community.
🔬 Research Areas
| Area | Focus | Key Datasets |
|---|---|---|
| 🎭 Deepfake Detection | Real-world faceswap detection beyond academic benchmarks | RWFS |
| 📄 Document Forgery | AI-inpainted receipts, forms, and financial documents | AIForge-Doc-v2 · AIForge-Doc-v1 · gpt4o-receipt |
| 🖼️ AI-Generated Image Detection | Self-reported AI-generated images in the wild | gpt-image-2 |
| 🛡️ Age Estimation Robustness | Cosmetic adversarial attacks against age verification | age-adversarial-attack |
| 👁️ Behavioral Biometrics | Gaze-based liveness for video interview verification | synthetic-gaze-reading |
📚 Featured Datasets
All datasets are released for academic research and non-commercial use under CC-BY-NC-SA 4.0. Email-gated download with automatic approval.
🎭 Deepfake Detection
- RWFS — Real-World Faceswap Dataset — 847 deepfakes from 8 production faceswap tools (Pixlr, Magic Hour, Remaker, etc) + 900 authentic faces. The first dataset reflecting how deepfakes actually appear in the wild.
Ren et al., "Do Deepfake Detectors Work in Reality?" — arXiv:2502.10920
📄 Document Forgery & Forensics
- AIForge-Doc v2 — 3,066 GPT-Image-2 inpainted document forgeries paired with authentic source + pixel-precise tampering masks. DocTamper-compatible.
- AIForge-Doc v1 — 4,061 forgeries via Gemini 2.5 / Ideogram v2. Same-spec pairing with v2 enables cross-generator detector analysis.
- GPT4o-Receipt — 935 fully AI-synthesized receipts (GPT-4o + GPT-Image-1) across 159 merchant categories. Companion human-vs-LLM forensic detection study.
🖼️ AI-Generated Image Detection
- GPT-Image-2 Twitter Dataset — 10,217 confirmed GPT-Image-2 outputs scraped from Twitter/X in the first week post-launch. Multi-language: EN (40%), JA (33%), ZH (19%).
🛡️ Identity Verification Robustness
- Age Adversarial Attack Dataset — 5,809 VLM-simulated cosmetic attacks (beard, gray hair, makeup, wrinkles) demonstrating 29–65% attack-conversion rate on production age estimators.
Ren et al., CVPR 2026
- Synthetic Eye Movement Dataset — 12 hours of synthetic eye-movement video (144 sessions × 5 min) for script-reading detection in video interviews.
📑 Publications
13 papers across deepfake detection, AI-generated detection, document forgery, age estimation, and interview technology. Browse the full list at scam.ai/research.
Selected work:
- Do Deepfake Detectors Work in Reality? — Ren, Patil, Zewde et al.
- AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents — Wu, Zhou, Xu et al. (arXiv:2602.20569)
- GPT-Image-2 in the Wild — Zewde, Ren, Shen et al. (arXiv:2604.25370)
- Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems — Shen, Duong, An et al. (arXiv:2602.19539, CVPR 2026)
💼 For Enterprise
The datasets above are released for the research community. For production needs we offer:
- Detection APIs — Deepfake, document forgery, AI-image, and age-verification endpoints with latency and accuracy SLAs
- On-premise deployment — Private cloud or air-gapped installations for regulated industries (banking, government, healthcare)
- Commercial licensing — Use our datasets and models in commercial pipelines
- Custom models — Trained on your domain, evaluated against the threat models we've published
📧 sales@scam.ai · 🌐 scam.ai
🤝 Get Involved
- ⭐ Follow this org to get notified of new dataset releases
- 📥 Download any dataset (free for non-commercial research, just provide name + email)
- 📝 Cite our papers if you publish work building on these resources
- 🐛 Open a discussion on any dataset to report issues or share results
Building detection systems for an era when generative AI makes every digital artifact suspect.