arXiv subjects

Zehong Yan

Publications and source records attributed to Zehong Yan.

3 recordsLinked to original sources

arXiv2025

TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection

Multimodal misinformation, encompassing textual, visual, and cross-modal distortions, poses an increasing societal threat that is amplified by generative AI. Existing methods typically focus on a single type of distortion and struggle to generalize to unseen scenarios. In this work, we observe that different distortion types share common reasoning capabilities while also requiring task-specific skills. We hypothesize that joint training across distortion types facilitates knowledge sharing and enhances the model's ability to generalize. To this end, we introduce TRUST-VL, a unified and explainable vision-language model for general multimodal misinformation detection. TRUST-VL incorporates a novel Question-Aware Visual Amplifier module, designed to extract task-specific visual features. To support training, we also construct TRUST-Instruct, a large-scale instruction dataset containing 198K samples featuring structured reasoning chains aligned with human fact-checking workflows. Extensive experiments on both in-domain and zero-shot benchmarks demonstrate that TRUST-VL achieves state-of-the-art performance, while also offering strong generalization and interpretability.

cs.CV↗

arXiv2025

Mitigating GenAI-Powered Evidence Pollution for Out-Of-Context Misinformation Detection

While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security. Out-of-context (OOC) multimodal misinformation detection systems typically rely on Web-retrieved evidence to identify images repurposed in false contexts, but they are increasingly challenged by the presence of GenAI-polluted evidence. Existing work mainly focuses on verifying claims that have undergone stylistic rewriting at the claim level and assume a clean evidence corpus. In this work, we remove this assumption and systematically study the impact of GenAI-driven evidence pollution threat on OOC detection. We show that polluted evidence can degrade the performance of state-of-the-art detectors by more than 9 percentage points. We propose two mitigating strategies, cross-modal evidence reranking and cross-modal claim-evidence reasoning, to address the challenge posed by polluted evidence. Extensive experiments on two benchmark datasets demonstrate that our approaches effectively enhance the robustness of existing OOC detectors amidst polluted evidence. The source code and data are publicly available at https://github.com/YanZehong/GenAI-Evidence-Pollution.

cs.MM↗

arXiv2024

SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection

Misinformation is a prevalent societal issue due to its potential high risks. Out-of-context (OOC) misinformation, where authentic images are repurposed with false text, is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments, which is essential for debunking misinformation. While Multimodal Large Language Models (MLLMs) have rich knowledge and innate capability for visual reasoning and explanation generation, they still lack sophistication in understanding and discovering the subtle crossmodal differences. In this paper, we introduce SNIFFER, a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation. SNIFFER employs two-stage instruction tuning on InstructBLIP. The first stage refines the model's concept alignment of generic objects with news-domain entities and the second stage leverages language-only GPT-4 generated OOC-specific instruction data to fine-tune the model's discriminatory powers. Enhanced by external tools and retrieval, SNIFFER not only detects inconsistencies between text and image but also utilizes external knowledge for contextual verification. Our experiments show that SNIFFER surpasses the original MLLM by over 40% and outperforms state-of-the-art methods in detection accuracy. SNIFFER also provides accurate and persuasive explanations as validated by quantitative and human evaluations.

cs.MM↗