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

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?'.

Try it in production

Scam AI's Eva-v1 model applies this research at production scale — 98.2% accuracy, under 4 seconds, 200 free detections per month.