DOI: 10.57760/sciencedb.29781
title: Benchmark and framework for continual AI-generated image detection
title: 面向AI生成图像持续检测基准数据集与框架研究

description: Objective In response to the risks of highly realistic image misuse arising from the rapid development of Artificial Intelligence Generated Content (AIGC) technology, and the challenges of existing detection methods struggling to adapt to continuously emerging new generative models and lacking continual learning capabilities, this paper constructs the first benchmark dataset for the continual detection of AI-generated images to address this challenge and proposes a corresponding continual detection framework.Methods First, we constructed a benchmark dataset for continual learning in AI-generated image detection, which includes samples from five mainstream generative models as well as real images, and is organized into a continual learning task stream. Second, we systematically defined and investigated the challenges faced by continual learning in this detection task, with a special focus on a novel "mixed binary- and single-class" incremental learning scenario that reflects real-world constraints. Based on this, we established three benchmarks with varying degrees of sample replay constraints. Finally, we adapted existing continual learning methods for each benchmark scenario and proposed a universal conversion framework for the most stringent no-replay setting to restore the efficacy of methods that fail under this condition.Results Experiments conducted on our proposed dataset validate the effectiveness of the benchmark and the methods. In scenarios permitting replay, the adapted methods successfully achieve incremental detection. In the strictest no-replay scenario, traditional non-replay methods suffer from severe performance degradation or even fail completely. In contrast, the application of our proposed universal conversion framework leads to a significant performance boost for these methods, effectively enhancing detection accuracy and source identification capabilities while substantially mitigating catastrophic forgetting.Conclusion This paper successfully constructs a benchmark for the continual detection of AI-generated images, provides an in-depth analysis of the key challenges involved, and proposes effective continual detection strategies and solutions, notably introducing an innovative framework for continual learning in no-replay scenarios. The findings of this research offer crucial methodological support and empirical evidence for the development of robust and adaptive detection systems capable of keeping pace with the ever-evolving landscape of AI generation technologies.
description: Objective In response to the risks of highly realistic image misuse arising from the rapid development of Artificial Intelligence Generated Content (AIGC) technology, and the challenges of existing detection methods struggling to adapt to continuously emerging new generative models and lacking continual learning capabilities, this paper constructs the first benchmark dataset for the continual detection of AI-generated images to address this challenge and proposes a corresponding continual detection framework.Methods First, we constructed a benchmark dataset for continual learning in AI-generated image detection, which includes samples from five mainstream generative models as well as real images, and is organized into a continual learning task stream. Second, we systematically defined and investigated the challenges faced by continual learning in this detection task, with a special focus on a novel "mixed binary- and single-class" incremental learning scenario that reflects real-world constraints. Based on this, we established three benchmarks with varying degrees of sample replay constraints. Finally, we adapted existing continual learning methods for each benchmark scenario and proposed a universal conversion framework for the most stringent no-replay setting to restore the efficacy of methods that fail under this condition.Results Experiments conducted on our proposed dataset validate the effectiveness of the benchmark and the methods. In scenarios permitting replay, the adapted methods successfully achieve incremental detection. In the strictest no-replay scenario, traditional non-replay methods suffer from severe performance degradation or even fail completely. In contrast, the application of our proposed universal conversion framework leads to a significant performance boost for these methods, effectively enhancing detection accuracy and source identification capabilities while substantially mitigating catastrophic forgetting.Conclusion This paper successfully constructs a benchmark for the continual detection of AI-generated images, provides an in-depth analysis of the key challenges involved, and proposes effective continual detection strategies and solutions, notably introducing an innovative framework for continual learning in no-replay scenarios. The findings of this research offer crucial methodological support and empirical evidence for the development of robust and adaptive detection systems capable of keeping pace with the ever-evolving landscape of AI generation technologies.