Continual Few-shot Adaptation for Synthetic Fingerprint Detection
The quality and realism of synthetically generated fingerprint images have increased significantly over the past decade fueled by advancements in generative artificial intelligence (GenAI). This has exacerbated the vulnerability of fingerprint recognition systems to data injection attacks, where synthetic fingerprints are maliciously inserted during enrollment or authentication. Hence, there is an urgent need for methods to detect if a fingerprint image is real or synthetic. While it is straightforward to train deep neural network (DNN) models to classify images as real or synthetic, often such DNN models overfit the training data and fail to generalize well when applied to synthetic fing...
| Authors | Joseph Geo Benjamin, Anil K. Jain, Karthik Nandakumar |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 4 May 2026 |
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