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.

Deepfake DetectionReal-World

Do deepfake detectors work in reality?

Simiao Ren, Disha Patil, Kidus Zewde, Dennis Ng, Hengwei Xu, Shengkai Jiang, Ramini Desai, Ning-Yau Cheng, Yining Zhou, Ragavi Muthukrishnan

Read on Google Scholar

How to cite

@inproceedings{ren2025deepfake,
  title={Do deepfake detectors work in reality?},
  author={Ren, Simiao and Patil, Disha and Zewde, Kidus and Ng, Tsang Dennis and Xu, Hengwei and Jiang, Shengkai and Desai, Ramini and Cheng, Ning-Yau and Zhou, Yining and Muthukrishnan, Ragavi},
  booktitle={Proceedings of the 4th workshop on security implications of deepfakes and cheapfakes},
  pages={21--26},
  year={2025}
}

Associated datasets

Real-World Faceswap Dataset (RWFS)

A real-world faceswap collection used to evaluate deepfake detectors against in-the-wild manipulations rather than lab-only synthetics.

Do deepfake detectors work in reality?

How to cite

Associated datasets

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