The rapid progress of generative AI has led to the emergence of new generative models, while existing detection methods struggle to keep pace, resulting in significant degradation in the detection performance. This highlights the urgent need for continuously updating AI-generated image detectors to adapt to new generators. To overcome low efficiency and catastrophic forgetting in detector updates, we propose LiteUpdate, a lightweight framework for updating AI-generated image detectors. LiteUpdate employs a representative sample selection module that leverages image confidence and gradient-based discriminative features to precisely select boundary samples. This approach improves learning and detection accuracy on new distributions with limited generated images, significantly enhancing detector update efficiency. Additionally, LiteUpdate incorporates a model merging module that fuses weights from multiple fine-tuning trajectories, including pre-trained, representative, and random updates. This balances the adaptability to new generators and mitigates the catastrophic forgetting of prior knowledge. Experiments demonstrate that LiteUpdate substantially boosts detection performance in various detectors. Specifically, on AIDE, the average detection accuracy on Midjourney improved from 87.63% to 93.03%, a 6.16% relative increase.

翻译：生成式AI的快速发展催生了新的生成模型，而现有的检测方法难以跟上步伐，导致检测性能显著下降。这凸显了持续更新AI生成图像检测器以适应新生成器的迫切需求。为解决检测器更新中的低效性和灾难性遗忘问题，我们提出了LiteUpdate，一种用于更新AI生成图像检测器的轻量级框架。LiteUpdate采用了一个代表性样本选择模块，该模块利用图像置信度和基于梯度的判别特征来精确选择边界样本。该方法通过有限的生成图像提高了对新分布的学习和检测精度，显著增强了检测器更新效率。此外，LiteUpdate集成了一个模型融合模块，该模块融合了来自多个微调轨迹的权重，包括预训练、代表性和随机更新。这平衡了对新生成器的适应性，并缓解了对先前知识的灾难性遗忘。实验表明，LiteUpdate在各种检测器中显著提升了检测性能。具体而言，在AIDE上，对Midjourney的平均检测准确率从87.63%提高到93.03%，相对提升了6.16%。