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Generalizable Dynamic Representation Learning for Source Identification in Sequential Data
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AI
DOI：10.1109/TKDE.2026.3667734.png)
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Abstract
En 中文
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 cross-domain 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.
Keywords:
Generalizable source identification
dynamic representation learning
sequential signal forensics
feature disentanglement
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