EfficientIML: Efficient High-Resolution Image Manipulation Localization

• URL: http://arxiv.org/abs/2509.08583v1
• Date: Wed, 10 Sep 2025 13:32:02 GMT
• Title: EfficientIML: Efficient High-Resolution Image Manipulation Localization
• Authors: Jinhan Li, Haoyang He, Lei Xie, Jiangning Zhang,
• Abstract summary: We propose a novel high-resolution SIF dataset of 1200+ diffusion-generated manipulations with semantically extracted masks.<n>We propose a novel EfficientIML model with a lightweight, three-stage EfficientRWKV backbone.<n>Our approach outperforms ViT-based and other SOTA lightweight baselines in localization performance, FLOPs and inference speed.
• Score: 38.432078329653926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With imaging devices delivering ever-higher resolutions and the emerging diffusion-based forgery methods, current detectors trained only on traditional datasets (with splicing, copy-moving and object removal forgeries) lack exposure to this new manipulation type. To address this, we propose a novel high-resolution SIF dataset of 1200+ diffusion-generated manipulations with semantically extracted masks. However, this also imposes a challenge on existing methods, as they face significant computational resource constraints due to their prohibitive computational complexities. Therefore, we propose a novel EfficientIML model with a lightweight, three-stage EfficientRWKV backbone. EfficientRWKV's hybrid state-space and attention network captures global context and local details in parallel, while a multi-scale supervision strategy enforces consistency across hierarchical predictions. Extensive evaluations on our dataset and standard benchmarks demonstrate that our approach outperforms ViT-based and other SOTA lightweight baselines in localization performance, FLOPs and inference speed, underscoring its suitability for real-time forensic applications.

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