Anchor-Based Multimodal Verification: A Dynamic Query Framework for Fake News Forensics in Short Videos

The proliferation of maliciously altered short videos on social media platforms poses a significant threat to information security ecosystems, eroding public trust in digital media. Despite recent advancements in detecting fake video news, significant challenges remain in the forensic analysis of short videos, leading to issues of bias. First, as technology rapidly advances, fake videos are becoming increasingly semantically convincing, undermining the effectiveness of current classification methods. Second, the heterogeneous nature of video modalities (visual, textual, audio) creates critical challenges for models to learn discriminative feature representations. To address these challenges, we propose a dynamic query framework for fake news forensics in short videos, termed the Semantic Guided Adaptive Network (SGAN). Our approach is motivated by the need to utilize superficial alignment to identify suspicious manipulations through anchor-based verification and to leverage the adaptive capability of learnable queries to learn the heterogeneous boundary in each modality. Specifically, SGAN comprises a verification module and a flexible query learning module. The verification module employs text as the anchor to verify detailed context, mining fine-grained information while emphasizing key features, thereby providing candidate manipulations for downstream modules. The query learning module leverages learnable queries to map heterogeneous forensic features and integrates them through multi-level fusion for decision-making. Extensive experiments conducted on two widely used datasets demonstrate the effectiveness and generalization of the proposed method. The code is released on github https://github.com/VILAN-Lab/SGAN