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.

AI DetectionGPT-Image-2Dataset

GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment

Kidus Zewde, Simiao Ren, Xingyu Shen, Jenny Wu, Yuchen Zhou, Tommy Duong, Zikang Zhang, Ethan Traister

Read on arXiv

How to cite

@article{zewde2026gptimage2,
  title={GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment},
  author={Zewde, Kidus and Ren, Simiao and Shen, Xingyu and Wu, Jenny and Zhou, Yuchen and Duong, Tommy and Zhang, Zikang and Traister, Ethan},
  journal={arXiv preprint arXiv:2604.25370},
  year={2026}
}

Associated datasets

GPT-Image-2 Twitter Dataset (GPT-image-2.0X)

Self-reported AI-generated images collected from Twitter during the first week of GPT-Image-2 deployment — captures real-world distribution shift.

GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment

How to cite

Associated datasets

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