取证感知的持续适应用于图像伪造定位

中文摘要 AI 辅助

图像处理技术的快速发展引发了日益增长的公共安全担忧。现有的图像伪造定位（IFL）方法能够准确定位被篡改的区域，但往往无法适应新出现的伪造类型。在现实世界的取证场景中，数据通常按顺序到达，然而在IFL中，模型的持续适应仍未得到充分探索。为填补这一空白，我们引入了首个用于IFL的持续学习框架，并在两种现实的数据演化协议下建立了全面的基准：跨数据集和跨内容的持续学习。对代表性最先进的IFL和持续学习方法的评估揭示了显著的性能退化，突出了两个关键挑战：（1）从跨未见领域到达的数据中自适应地捕获内在的取证痕迹；（2）在顺序适应过程中保留先前获取的取证知识。为应对这些挑战，我们提出了一种取证感知的持续适应框架。首先，一个取证痕迹挖掘模块采用空间混合取证专家（SMoFE）来动态路由跨空间位置的互补取证线索，并结合取证证据引导的密集提示（FEGDP）将低级取证痕迹转化为SAM的结构化定位证据。其次，Fisher加权LoRA梯度（FLAG）手术识别旧任务敏感的适应方向并抑制冲突更新，减轻灾难性遗忘，同时保持对新出现的伪造领域的可塑性。大量实验证明，在各种持续学习场景中，该方法在像素级伪造定位和图像级伪造检测方面均达到了最先进的性能。

英文摘要

The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods can accurately localize manipulated regions but are often unable to adapt to newly emerging forgeries. In real-world forensic scenarios, data typically arrive sequentially, yet continual model adaptation remains largely unexplored in IFL. To bridge this gap, we introduce the first continual learning framework for IFL and establish a comprehensive benchmark under two realistic data-evolution protocols: cross-dataset and cross-content continual learning. Evaluations of representative state-of-the-art IFL and continual learning methods reveal substantial performance degradation, highlighting two key challenges: (1) adaptively capturing intrinsic forensic traces from incoming data across unseen domains, and (2) preserving previously acquired forensic knowledge during sequential adaptation. To address these challenges, we propose a forensic-aware continual adaptation framework. First, a forensic trace mining module employs Spatial Mixture-of-Forensic-Experts (SMoFE) to dynamically route complementary forensic cues across spatial locations, together with Forensic Evidence-Guided Dense Prompting (FEGDP) to transform low-level forensic traces into structured localization evidence for SAM. Second, Fisher-weighted LoRA Gradient (FLAG) surgery identifies old-task-sensitive adaptation directions and suppresses conflicting updates, mitigating catastrophic forgetting while preserving plasticity for emerging forgery domains. Extensive experiments demonstrate state-of-the-art performance in both pixel-level forgery localization and image-level forgery detection across diverse continual learning scenarios.

发表机构

• National University of Singapore（新加坡国立大学）
• Nanyang Technological University（南洋理工大学）
• Jiangxi University of Finance and Economics（江西财经大学）
• Sichuan University（四川大学）

机构由 AI 辅助整理，请以论文原文为准。