弥合微-宏观差距：面向图像操纵本地化的频率感知语义对齐
Bridging the Micro--Macro Gap: Frequency-Aware Semantic Alignment for Image Manipulation Localization
AI总结：
本文提出FASA框架，通过自适应双频带DCT模块提取操纵敏感频率特征，结合冻结CLIP表示的补丁级对比对齐学习语义先验，实现传统和扩散生成操纵的精准本地化。
摘要：
随着生成图像编辑的发展，图像操纵本地化（IML）必须处理传统操纵的明显取证痕迹和扩散生成的局部真实编辑。现有方法通常依赖低级取证线索或高级语义，导致微-宏观差距。为弥合此差距，我们提出FASA，一个统一框架用于本地化传统和扩散生成的操纵。具体而言，我们通过自适应双频带DCT模块提取操纵敏感频率特征，并通过冻结CLIP表示的补丁级对比对齐学习操纵感知的语义先验。然后，我们将这些先验注入到层次化的频率路径中，通过语义-频率侧适配器进行多尺度特征交互，并采用原型引导的频率门控掩码解码器，整合语义一致性与边界感知的本地化，以预测篡改区域。在OpenSDI和多个传统操纵基准上的广泛实验表明，该方法在本地化性能、跨生成器和跨数据集泛化能力以及常见图像退化下的鲁棒性方面均达到最先进水平。
英文摘要：
As generative image editing advances, image manipulation localization (IML) must handle both traditional manipulations with conspicuous forensic artifacts and diffusion-generated edits that appear locally realistic. Existing methods typically rely on either low-level forensic cues or high-level semantics alone, leading to a fundamental micro--macro gap. To bridge this gap, we propose FASA, a unified framework for localizing both traditional and diffusion-generated manipulations. Specifically, we extract manipulation-sensitive frequency cues through an adaptive dual-band DCT module and learn manipulation-aware semantic priors via patch-level contrastive alignment on frozen CLIP representations. We then inject these priors into a hierarchical frequency pathway through a semantic-frequency side adapter for multi-scale feature interaction, and employ a prototype-guided, frequency-gated mask decoder to integrate semantic consistency with boundary-aware localization for tampered region prediction. Extensive experiments on OpenSDI and multiple traditional manipulation benchmarks demonstrate state-of-the-art localization performance, strong cross-generator and cross-dataset generalization, and robust performance under common image degradations.
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。
