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.

Age EstimationAdversarialBenchmark

Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems

Xingyu Shen, Tommy Duong, Xiaodong An, Zengqi Zhao, Zebang Hu, Haoyu Hu, Ziyou Wang, Finn Guo, Simiao Ren

Read on arXiv

How to cite

@article{shen2026can,
  title={Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems},
  author={Shen, Xingyu and Duong, Tommy and An, Xiaodong and Zhao, Zengqi and Hu, Zebang and Hu, Haoyu and Wang, Ziyou and Guo, Finn and Ren, Simiao},
  journal={arXiv preprint arXiv:2602.19539},
  year={2026}
}

Associated datasets

Adversarial age estimation attack dataset

Faces with low-cost cosmetic adversarial perturbations designed to defeat age estimation systems — used in 'Can a Teenager Fool an AI?'.

Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems

How to cite

Associated datasets

More from Age Estimation