ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection

TL;DR

ForgeLens is a novel, data-efficient forgery detection framework that enhances generalization to unseen forgery techniques by guiding feature extraction and integrating forgery-specific information, achieving state-of-the-art results with minimal training data.

Contribution

It introduces a lightweight, feature-guided framework with guidance and integration modules to improve forgery detection generalization and data efficiency.

Findings

Achieves 13.61% higher accuracy over the base model.

Outperforms existing methods with only 1% training data.

Demonstrates strong generalization across 19 generative models.

Abstract

The rise of generative models has raised concerns about image authenticity online, highlighting the urgent need for a detector that is (1) highly generalizable, capable of handling unseen forgery techniques, and (2) data-efficient, achieving optimal performance with minimal training data, enabling it to counter newly emerging forgery techniques effectively. To achieve this, we propose ForgeLens, a data-efficient, feature-guided framework that incorporates two lightweight designs to enable a frozen network to focus on forgery-specific features. First, we introduce the Weight-Shared Guidance Module (WSGM), which guides the extraction of forgery-specific features during training. Second, a forgery-aware feature integrator, FAFormer, is used to effectively integrate forgery information across multi-stage features. ForgeLens addresses a key limitation of previous frozen network-based methods, where general-purpose features extracted from large datasets often contain excessive forgery-irrelevant information. As a result, it achieves strong generalization and reaches optimal performance with minimal training data. Experimental results on 19 generative models, including both GANs and diffusion models, demonstrate improvements of 13.61% in Avg.Acc and 8.69% in Avg.AP over the base model. Notably, ForgeLens outperforms existing forgery detection methods, achieving state-of-the-art performance with just 1% of the training data. Our code is available at https://github.com/Yingjian-Chen/ForgeLens.