以生成器感知原型扩展AI生成图像检测
Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes
用一组紧凑的"伪造原型"把真实/合成图像特征压到低方差统一空间，再用LoRA两阶段微调预训练编码器，可直接迁移到任何需要跨生成器泛化的取证或真伪判别任务。
原文依据 learns a compact set of canonical forgery prototypes to create a unified, low-variance feature space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论追求通用AI生成图像（AIGI）检测器通常依赖聚合来自众多生成器的数据以提升泛化能力。然而，本文发现了一种被称为"先受益后冲突"的两难现象：随着来源多样性的扩大，检测器性能趋于停滞并最终下降。我们的系统性分析通过识别两个核心问题来诊断这一失效：严重的数据层异构性使真实图像与合成图像的特征分布日益重叠；以及来自固定的预训练编码器的关键模型层瓶颈，其无法适应不断上升的复杂度。为应对这些挑战，我们提出生成器感知原型学习（GAPL），一个以结构化学习范式约束表征的框架。GAPL学习一小组紧凑的规范伪造原型，以构建统一、低方差的特征空间，从而有效对抗数据异构性。为解决模型瓶颈，它采用结合低秩适配的两阶段训练方案，在保留有价值的预训练知识的同时增强判别能力。该方法建立了更稳健、更具泛化性的决策边界。通过大量实验，我们证明GAPL达到最优性能，在多种生成对抗网络与基于扩散的生成器上展现出更优的检测准确率。代码见 https://github.com/UltraCapture/GAPL
英文原文摘要 1477 字符
The pursuit of a universal AI-generated image (AIGI) detector often relies on aggregating data from numerous generators to improve generalization. However, this paper identifies a paradoxical phenomenon we term the "Benefit then Conflict" dilemma, where detector performance stagnates and eventually degrades as source diversity expands. Our systematic analysis, diagnoses this failure by identifying two core issues: severe data-level heterogeneity which causes the feature distributions of real and synthetic images to increasingly overlap, and a critical model-level bottleneck from fixed, pretrained encoders that cannot adapt to the rising complexity. To address these challenges, we propose Generator-Aware Prototype Learning (GAPL) , a framework that constrain representation with a structured learning paradigm. GAPL learns a compact set of canonical forgery prototypes to create a unified, low-variance feature space, effectively countering data heterogeneity. To resolve the model bottleneck, it employs a two-stage training scheme with Low-Rank Adaptation, enhancing its discriminative power while preserving valuable pretrained knowledge. This approach establishes a more robust and generalizable decision boundary. Through extensive experiments, we demonstrate that GAPL achieves state-of-the-art performance, showing superior detection accuracy across a wide variety of GAN and diffusion-based generators. Code is available at https://github.com/UltraCapture/GAPL