Generalizable and Adaptive Continual Learning Framework for AI-generated Image Detection

2026-01-09

IEEE transactions on multimedia

AI Summary

This work addresses the poor generalization of existing AI-generated image detection methods in the face of rapidly evolving generative models. The authors propose the first three-stage continual learning framework tailored for this task: first, a parameter-efficient fine-tuning strategy is employed to build a strong offline detector with enhanced generalization; second, catastrophic forgetting is mitigated through progressive-complexity data augmentation combined with K-FAC–approximated Hessian regularization; third, linear mode connectivity interpolation is leveraged to improve cross-model transferability. Evaluated on a comprehensive benchmark encompassing 27 generative models, the proposed offline detector achieves a 5.51% mAP improvement over baseline methods, and the continual learning phase attains an average accuracy of 92.20%, substantially outperforming current state-of-the-art approaches.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The malicious misuse and widespread dissemination of AI-generated images pose a significant threat to the authenticity of online information. Current detection methods often struggle to generalize to unseen generative models, and the rapid evolution of generative techniques continuously exacerbates this challenge. Without adaptability, detection models risk becoming ineffective in real-world applications. To address this critical issue, we propose a novel three-stage domain continual learning framework designed for continuous adaptation to evolving generative models. In the first stage, we employ a strategic parameter-efficient fine-tuning approach to develop a transferable offline detection model with strong generalization capabilities. Building upon this foundation, the second stage integrates unseen data streams into a continual learning process. To efficiently learn from limited samples of novel generated models and mitigate overfitting, we design a data augmentation chain with progressively increasing complexity. Furthermore, we leverage the Kronecker-Factored Approximate Curvature (K-FAC) method to approximate the Hessian and alleviate catastrophic forgetting. Finally, the third stage utilizes a linear interpolation strategy based on Linear Mode Connectivity, effectively capturing commonalities across diverse generative models and further enhancing overall performance. We establish a comprehensive benchmark of 27 generative models, including GANs, deepfakes, and diffusion models, chronologically structured up to August 2024 to simulate real-world scenarios. Extensive experiments demonstrate that our initial offline detectors surpass the leading baseline by +5.51% in terms of mean average precision. Our continual learning strategy achieves an average accuracy of 92.20%, outperforming state-of-the-art methods.

Problem

• AI-generated image detection
• generalization
• continual learning
• adaptability
• catastrophic forgetting

Innovation

• Continual Learning
• AI-generated Image Detection
• Parameter-efficient Fine-tuning
• K-FAC
• Linear Mode Connectivity