标题： 提出与修正：一种基于取证的MLLM图像篡改定位框架 显示英文标题

标题： Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization

摘要： 图像处理技术的日益复杂化要求强大的取证解决方案，能够可靠地检测篡改并精确定位被篡改区域。 最近的多模态大语言模型（MLLMs）通过利用世界知识和语义理解进行上下文感知检测展现出前景，但它们在感知对于准确篡改定位至关重要的细微低级取证特征方面存在困难。 本文提出了一种新颖的提议-修正框架，能够有效弥合语义推理与取证特定分析之间的差距。 在提议阶段，我们的方法利用适应取证的LLaVA模型，基于语义理解和上下文推理生成初始篡改分析和可疑区域的初步定位。 在修正阶段，我们引入了一个取证修正模块，通过多尺度取证特征分析系统地验证和优化这些初始提议，并整合来自多个专业过滤器的技术证据。 此外，我们提出了一种增强的分割模块，将关键的取证线索融入SAM的编码图像嵌入中，从而克服固有的语义偏差，实现对被篡改区域的精确划分。 通过协同结合先进的多模态推理与已建立的取证方法，我们的框架确保初始的语义提议通过具体的的技术证据进行系统验证和增强，从而实现全面的检测准确性和定位精度。 广泛的实验验证表明，在多种数据集上表现出最先进的性能，具有出色的鲁棒性和泛化能力。 显示英文摘要

摘要： The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models (MLLMs) show promise by leveraging world knowledge and semantic understanding for context-aware detection, yet they struggle with perceiving subtle, low-level forensic artifacts crucial for accurate manipulation localization. This paper presents a novel Propose-Rectify framework that effectively bridges semantic reasoning with forensic-specific analysis. In the proposal stage, our approach utilizes a forensic-adapted LLaVA model to generate initial manipulation analysis and preliminary localization of suspicious regions based on semantic understanding and contextual reasoning. In the rectification stage, we introduce a Forensics Rectification Module that systematically validates and refines these initial proposals through multi-scale forensic feature analysis, integrating technical evidence from several specialized filters. Additionally, we present an Enhanced Segmentation Module that incorporates critical forensic cues into SAM's encoded image embeddings, thereby overcoming inherent semantic biases to achieve precise delineation of manipulated regions. By synergistically combining advanced multimodal reasoning with established forensic methodologies, our framework ensures that initial semantic proposals are systematically validated and enhanced through concrete technical evidence, resulting in comprehensive detection accuracy and localization precision. Extensive experimental validation demonstrates state-of-the-art performance across diverse datasets with exceptional robustness and generalization capabilities.