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A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification.

Yih-Kai Lin¹, Ting-Yu Yen¹

¹Department of Computer Science and Artificial Intelligence, National Pingtung University, No. 4-18 Minsheng Road, Pingtung City 90003, Taiwan.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This study introduces a meta-learning approach for detecting novel image forgeries. The method enhances adaptability, allowing detectors to identify new forgery techniques with minimal new samples.

Keywords:

U-Net digital forensics face forgery detection few-shot learning meta-learning segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Digital Forensics

Background:

Current forged image detection technology excels with known methods but struggles with novel forgery techniques.
Existing approaches using hand-crafted generators have limited detection capabilities for unseen forging methods.

Purpose of the Study:

To develop a highly adaptive detector for identifying new forging techniques using a meta-learning approach.
To overcome the limitations of current methods in detecting previously unseen image forgeries.

Main Methods:

A meta-learning approach is employed to train a forged image detector.
The detector is fine-tuned using a small number of new forged samples.
The method adjusts detector weights based on statistical features of input forged images.

Main Results:

Significant improvements in detecting forgery methods were achieved.
Intersection over Union (IoU) improved by 35.4% to 127.2%.
Area Under the Curve (AUC) improved by 2.0% to 48.9%.

Conclusions:

The proposed meta-learning method significantly enhances detection performance with limited new samples.
The approach demonstrates superior performance compared to state-of-the-art methods for detecting novel image forgeries.