发表机构
IIIT-Hyderabad; IISER-Bhopal（印度国际信息技术研究所海得拉巴分校; 印度科学教育与研究研究所博帕尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像真实性验证难题，提出SegWave混合框架，结合Transformer与DWT并引入ASA模块，经多基准数据集实验，其性能优于现有最先进的篡改检测方法。
AI 中文摘要
图像真实性验证日益困难，在新闻、执法和政治领域带来严重风险。现有大多数取证方法依赖高层视觉伪影，将帧检测视为简单的二分类任务。为解决该问题，我们提出SegWave，一种联合利用空间和频域线索的混合图像篡改检测框架。SegWave将基于Transformer的架构与离散小波变换（DWT）相结合，以捕捉指示篡改的局部多尺度频率不一致性。为进一步提升定位效果，我们引入自适应子带注意力模块（ASA），该模块可动态突出具有信息价值的高频小波分量。在多个基准数据集上开展的大量实验表明，SegWave在挑战性评估设置中始终优于最先进的篡改检测方法。
英文摘要
Verifying image authenticity is increasingly difficult, posing serious risks across journalism, law enforcement, and political domains. Most existing forensic methods rely on high-level visual artifacts and treat frame detection as a simple binary task. To address this, we propose SegWave, a hybrid framework that jointly leverages spatial and frequency-domain cues for image tampering detection. SegWave integrates a transformer-based architecture with the Discrete Wavelet Transform (DWT) to capture localized, multi-scale frequency inconsistencies indicative of manipulation. To further improve localization effectiveness, we introduce an Adaptive Sub-band Attention module (ASA) that dynamically highlights the informative high-frequency wavelet components. Extensive experiments on multiple benchmark datasets demonstrate that SegWave consistently outperforms state-of-the-art tampering detection methods in challenging evaluation settings.
CommentsAccepted at the PFATCV Workshop, ECCV 2026
