SegWave: ウェーブレット駆動による改ざん領域のセグメンテーション
SegWave: Wavelet-Driven Segmentation of Tampered Regions
画像改ざん検出のためのハイブリッドフレームワークで、空間的特徴と周波数領域の手がかりを組み合わせ、離散ウェーブレット変換と適応サブバンド注意機構を用いて改ざん領域を高精度に特定する。
著者: Siddhi Pravin Lipare, Vishesh Kumar, Akshay Agarwal
分類: cs.CV
原文アブストラクト
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.