SOURCE-LINKED INTELLIGENCE
SegWave: Wavelet-Driven Segmentation of Tampered Regions
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 l
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T12:54:11.000Z
First collected: 2026-09-21T07:01:58.596Z. This is not the publication date.