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.

Source identification is a foundational task in multimedia forensics, enabling the attribution and verification of digital content. While existing methods have achieved significant progress for static data, they often fail to generalize effectively on sequential data, which exhibit unique challenges such as temporal dependencies and dynamic variations caused by environmental and transmission factors. These challenges are further exacerbated in real-world scenarios, where crossdomain variations-spanning devices, software, and transmission protocols-significantly degrade the performance of traditional approaches. To address these limitations, we propose VoVAE, a probabilistic variational framework tailored for generalizable source identification in sequential data. VoVAE explicitly models temporal dependencies while disentangling dynamic variations (e.g., transmission distortions) from static source-specific features (e.g., device patterns) within a decoupled but complementary feature space. By separating these factors, VoVAE enables the extraction of robust and transferable representations, ensuring accurate source attribution across diverse and unseen conditions. We evaluate VoVAE on two challenging forensic applications: cross-domain VoIP phone call identification and cross-domain video source camera identification, using the VPCID and QUFVD datasets. Experimental results demonstrate that VoVAE outperforms state-of-the-art methods, achieving significant improvements in generalization across cross-device, cross-software, and cross-brand scenarios. Comprehensive ablation studies further highlight the importance of dynamic representation learning and feature disentanglement in capturing temporal patterns and enhancing robustness to domain shifts. These findings establish VoVAE as a scalable and robust solution for source identification in sequential data across diverse forensic scenarios.