ScamAI is an AI-powered platform for detecting deepfakes and synthetic media in real-time. We provide AI detection, audio detection, and GenAI content verification with industry-leading accuracy.

How accurate is ScamAI's deepfake detection?

ScamAI's Eva-v1 models are continuously trained on the latest synthetic media techniques with processing times under 4 seconds. Accuracy varies by media type and attack technique — contact us for benchmark details relevant to your use case.

Is ScamAI compliant with security standards?

Yes, ScamAI is SOC 2 Type II compliant and GDPR compliant, ensuring enterprise-grade security and data protection.

How much does ScamAI cost?

ScamAI offers 200 free images per month for free. Pay-as-you-go pricing starts at $0.05 per image. Optional add-ons (face attributes, liveness detection, ID document verification) are $0.008 per image each. Enterprise pricing is available for high-volume users.

How does ScamAI compare to other deepfake detection tools?

ScamAI offers both vision and audio detection in a single platform with sub-4-second processing, and provides a developer-friendly API with integration in under 10 minutes. Unlike many competitors, ScamAI offers a free tier of 200 images per month, transparent per-image pricing, and is SOC 2 Type II and GDPR compliant.

What industries use ScamAI?

ScamAI is used across financial services for KYC verification, content platforms for moderation, call centers for voice authentication, media organizations for content verification, HR departments for remote interview verification, insurance for claims validation, and government agencies for public sector security.

Dataset

AI-edit document forgery dataset (AIForge-Doc)

Financial and form documents tampered by a suite of AI editing tools, paired with originals — used to benchmark document forgery detectors.

View paper

How to cite

@article{wu2026aiforge,
  title={AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents},
  author={Wu, Jiaqi and Zhou, Yuchen and Xu, Muduo and Liang, Zisheng and Ren, Simiao and Xue, Jiayu and Yang, Meige and Chen, Siying and Huan, Jingheng},
  journal={arXiv preprint arXiv:2602.20569},
  year={2026}
}

Usage terms

Use solely for non-commercial research purposes.
Cite the associated publication in any resulting work.
Do not redistribute the dataset.

Other research datasets

Real-World Faceswap Dataset (RWFS)
AIForge-Doc v2.0 (GPT-Image-2 document forgeries)
GPT-Image-2 Twitter Dataset (GPT-image-2.0X)
Adversarial age estimation attack dataset
Fully-synthetic AI-generated receipt (GPT-4o-receipt)
Simulated gaze estimation for reading dataset