順序依存クロスブランチ相互作用による画像改ざん位置特定
OCBI: Order-Sensitive Cross-Branch Interaction for Image Manipulation Localization
RGB誘導のクロスアテンションとフォレンジック自己アテンションの合成順序の違いを交換子でモデル化し、改ざんされたピクセルの位置特定精度を向上させた研究。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhouli Yu
分類: cs.CV
原文アブストラクト
Image manipulation localization combines semantic structure with forensic traces to identify modified pixels. Within-branch spatial mixing and RGB-guided cross-branch mixing can produce different messages when composed in opposite orders. We propose OCBI, an extension of Co-Transformers that models the discrepancy between these two composition orders for localization. A local cross-branch fusion module uses the commutator of RGB-guided cross-attention and forensic self-attention to add a learnable signed correction to feature transfer. Pixelwise adaptive multiscale aggregation then combines spatial contexts before prediction. OCBI uses the Co-Transformers backbone and supervision. We compare OCBI with retrained Co-Transformers using CASIA-only task training, a 50-epoch budget, and final-epoch evaluation. Across five clean test datasets, OCBI achieves a macro PixelF1 of 0.5773, exceeding the baseline by 0.43 percentage points. Under JPEG compression, Gaussian blur, and resizing on three datasets, its average PixelF1 over 21 dataset-transformation conditions exceeds the baseline by 0.95 percentage points.
