用小波变换和注意力机制精准定位图像篡改区域
SegWave: Wavelet-Driven Segmentation of Tampered Regions
- 结合小波变换与Transformer捕捉多尺度频率异常
- 在多个数据集上准确率超越现有方法
- 适合图像取证、媒体审核等需要精确定位的场景
图像真实性验证日益困难，给新闻、执法和政治领域带来严重风险。现有检测方法多依赖高层视觉痕迹，将篡改检测视为简单二分类任务。为此，我们提出SegWave，一种融合空间与频域信息的混合框架。该框架结合基于Transformer的结构与离散小波变换（DWT），捕捉局部、多尺度的频率不一致特征，以识别篡改痕迹。为进一步提升定位精度，引入自适应子带注意力模块（ASA），动态强化具有信息量的高频小波成分。在多个基准数据集上的大量实验表明，SegWave在复杂评估场景下持续优于当前最优的篡改检测方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。