从被动感知到主动记忆：一种由粗粒注释驱动的弱监督图像篡改定位框架
From Passive Perception to Active Memory: A Weakly Supervised Image Manipulation Localization Framework Driven by Coarse-Grained Annotations
AI总结：
BoxPromptIML通过粗粒注释策略和双指导特征融合，实现低注释成本下的高精度图像篡改定位。
摘要：
图像篡改定位（IML）面临一个根本性的权衡，即在最小化注释成本和实现细粒度定位精度之间。现有的全监督IML方法严重依赖于密集的像素级掩码注释，这限制了其扩展性以适应大规模数据集或实际应用。相比之下，现有大多数弱监督IML方法基于图像级标签，这大大减少了注释努力，但通常缺乏精确的空间定位。为了解决这一困境，我们提出了BoxPromptIML，一种新的弱监督IML框架，能够有效平衡注释成本和定位性能。具体而言，我们提出了一种粗粒区域注释策略，可以以较低的成本生成相对准确的篡改掩码。为了提高模型效率并促进部署，我们进一步设计了一个高效的轻量级学生模型，该模型通过从基于Segment Anything Model（SAM）的固定教师模型进行知识蒸馏，学习执行细粒度定位。此外，受人类潜意识记忆机制的启发，我们的特征融合模块采用双指导策略，将回忆的原型模式与输入中实时观察线索相结合。与被动特征提取不同，这种策略使知识回忆过程动态化，其中长期记忆适应于当前图像的具体上下文，显著提高了定位精度和鲁棒性。在包含内分布和外分布数据集的广泛实验中，BoxPromptIML在性能上优于或与全监督模型相当，同时保持了强大的泛化能力、低注释成本和高效的部署特性。
英文摘要：
Image manipulation localization (IML) faces a fundamental trade-off between minimizing annotation cost and achieving fine-grained localization accuracy. Existing fully-supervised IML methods depend heavily on dense pixel-level mask annotations, which limits scalability to large datasets or real-world deployment.In contrast, the majority of existing weakly-supervised IML approaches are based on image-level labels, which greatly reduce annotation effort but typically lack precise spatial localization. To address this dilemma, we propose BoxPromptIML, a novel weakly-supervised IML framework that effectively balances annotation cost and localization performance. Specifically, we propose a coarse region annotation strategy, which can generate relatively accurate manipulation masks at lower cost. To improve model efficiency and facilitate deployment, we further design an efficient lightweight student model, which learns to perform fine-grained localization through knowledge distillation from a fixed teacher model based on the Segment Anything Model (SAM). Moreover, inspired by the human subconscious memory mechanism, our feature fusion module employs a dual-guidance strategy that actively contextualizes recalled prototypical patterns with real-time observational cues derived from the input. Instead of passive feature extraction, this strategy enables a dynamic process of knowledge recollection, where long-term memory is adapted to the specific context of the current image, significantly enhancing localization accuracy and robustness. Extensive experiments across both in-distribution and out-of-distribution datasets show that BoxPromptIML outperforms or rivals fully-supervised models, while maintaining strong generalization, low annotation cost, and efficient deployment characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
