具有门控注入的频率感知语义融合用于AI生成图像检测
Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection
AI总结：
本文提出FGINet，通过BMFE和LGFI机制提升AI生成图像检测的泛化能力，采用HCL框架学习紧凑且分离的表示。
摘要：
AI生成图像日益逼真且多样，对通用检测构成重大挑战。尽管视觉基础模型（VFMs）提供丰富的语义表示，频率方法捕捉互补的伪影线索，现有结合这些模态的方法仍面临泛化能力有限的问题。本文提出频率感知门控注入网络（FGINet），设计带频域交叉掩码的BMFE以减少对特定生成器模式的依赖，引入逐层门控频率注入机制以渐进注入频率线索，同时提出超球面紧凑性学习框架以学习紧凑且分离的表示。实验表明，FGINet在多个挑战性数据集上均取得最先进的性能。
英文摘要：
AI-generated images are becoming increasingly realistic and diverse, posing significant challenges for generalizable detection. While Vision Foundation Models (VFMs) provide rich semantic representations and frequency-based methods capture complementary artifact cues, existing approaches that combine these modalities still suffer from limited generalization, with notable performance degradation on unseen generative models. We attribute this limitation to two key factors: frequency shortcut bias toward easily distinguishable cues associated with specific generators and cross-domain representation conflict between high-level semantics and low-level frequency patterns. To address these issues, we propose a Frequency-aware Gated Injection Network (FGINet) to improve generalization. Specifically, we design a Band-Masked Frequency Encoder (BMFE) that applies cross-band masking in the frequency domain to reduce reliance on generator-specific patterns and encourage more diverse and generalizable representations. We further introduce a Layer-wise Gated Frequency Injection (LGFI) mechanism to progressively inject frequency cues into the VFM backbone with adaptive gating, aligning with its hierarchical abstraction and alleviating representation conflict. Moreover, we propose a Hyperspherical Compactness Learning (HCL) framework with a cosine margin objective to learn compact and well-separated representations. Extensive experiments demonstrate that FGINet achieves state-of-the-art performance and strong generalization across multiple challenging datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
