Comparative Analysis of Zero-Shot Learning Techniques for Fake Image Detection: A Results-Oriented Review Analysis.

Authors

  • Roshani Parate and Dr.Kirti Jain

Keywords:

ZSL, prompt learning, deepfake

Abstract

The rapid advancement of AI-generated image synthesis has led to an increased prevalence of fake images,posing significant challenges for authenticity verification. Traditional fake image detection methods often rely on supervised learning, which demands extensive labeled datasets and struggles

References

Cozzolino, D., Poggi, G., Nießner, M., & Verdoliva, L. (2024). Zero-shot detection of AI-generated images. arXiv. https://doi.org/10.48550/arXiv.2409.15875

Liu, W., Shen, X., Pun, C.-M., & Cun, X. (2024). ForgeryTTT: Zero-shot image manipulation localization with test-time training. arXiv. https://arxiv.org/abs/2410.04032

Downloads

Published

2025-05-13

How to Cite

Roshani Parate and Dr.Kirti Jain. (2025). Comparative Analysis of Zero-Shot Learning Techniques for Fake Image Detection: A Results-Oriented Review Analysis . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1017–1023. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2653

Issue

Vol. 34 No. 4 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.