Few-shot learning in realistic settings for text CAPTCHA recognition

Text-based captcha is commonly used by many commercial websites. Most existing captcha recognition methods rely on deep learning and large-scale labeled data. Recently, few-shot learning has shown its effectiveness in various visual classification tasks in the case of insufficient data. However, the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neural computing & applications 2023-05, Vol.35 (15), p.10751-10764
Hauptverfasser: Wang, Yao, Wei, Yuliang, Zhang, Yifan, Jin, Chuhao, Xin, Guodong, Wang, Bailing
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Text-based captcha is commonly used by many commercial websites. Most existing captcha recognition methods rely on deep learning and large-scale labeled data. Recently, few-shot learning has shown its effectiveness in various visual classification tasks in the case of insufficient data. However, the performance of current few-shot learning methods will deteriorate in realistic settings with class-imbalance and cross-domain. In this paper, we have proposed a novel captcha solver based on prototypical networks and model-agnostic meta-learning. Two major improvements, including multi-source domain data augmentation and intra-class variance distance weighting method, are proposed to alleviate the performance degradation problems caused by cross-domain and class imbalance. Our approaches achieve an average character accuracy of more than 90% in 5-shot and 10-shot tasks and an astonishing attack rate of 88% in one-shot tasks. The efficacy of this work may promote the application of few-shot learning in realistic settings.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08262-0