SegWave: Wavelet-Driven Segmentation of Tampered Regions
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed SegWave, a method to find fake parts in images by looking at both the picture itself and its hidden frequency details. They use a special technique called the Discrete Wavelet Transform to spot unusual patterns that might mean an image was altered. They also created a tool called Adaptive Sub-band Attention to focus on the most important parts of the frequency information. Tests show their approach works better than older methods at detecting image tampering.
Image authenticityImage tampering detectionDiscrete Wavelet TransformTransformer architectureFrequency-domain analysisSpatial-domain analysisAdaptive Sub-band AttentionHigh-frequency componentsLocalization in imagesVisual forensics
Authors
Siddhi Pravin Lipare, Vishesh Kumar, Akshay Agarwal
Abstract
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.