Spotting AI-Made Images Without Constant Retraining

Kari Jaaskelainen

21 Nov 2025 — 1 min read

Spotting AI-made images—without constant retraining

Generative models evolve fast, making deepfake detectors go stale. This study introduces a two-stage method that adapts quickly to new image generators.

Stage 1: A vision model learns subtle “fingerprints” via supervised contrastive learning, trained while holding out some generator families to force cross-model generalization.
Stage 2: A lightweight k-NN classifier is fit in a few-shot setup using about 150 images per class from a new generator—no heavy retraining.

Results: 91.3% detection accuracy with just a handful of samples, a +5.2 pp gain over prior methods. For source attribution in an open-set setting, it boosts AUC by +14.70% and OSCR by +4.27%.

Why it matters: detectors can stay effective as new models appear, using readily obtainable examples, keeping forensic tools robust and scalable.

Paper by Jaime Álvarez Urueña, David Camacho, Javier Huertas Tato. arxiv.org/abs/2511.16541

Paper: https://arxiv.org/abs/2511.16541v1

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AI DeepfakeDetection GenerativeAI ComputerVision MachineLearning TrustAndSafety AIDetection FewShotLearning

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Spotting AI-made images—without constant retraining

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