# FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering Source: [FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2026-02-04T05:00:04.387Z Authors: Chaodong Tong, Qi Zhang, Chen Li, Lei Jiang, Yanbing Liu Published: 2026-01-01T09:19:39.000Z ## Abstract Faithfulness hallucinations in VQA occur when vision-language models produce fluent yet visually ungrounded answers, severely undermining their reliability in safety-critical applications. Existing detection methods mainly fall into two categories: external verification approaches relying on auxiliary models or knowledge bases, and uncertainty-driven approaches using repeated sampling or uncertainty estimates. The former suffer from high computational overhead and are limited by external resource quality, while the latter capture only limited facets of model uncertainty and fail to sufficiently explore the rich internal signals associated with the diverse failure modes. Both paradigms thus have inherent limitations in efficiency, robustness, and detection performance. To address these challenges, we propose FaithSCAN: a lightweight network that detects hallucinations by exploiting rich internal signals of VLMs, including token-level decoding uncertainty, intermediate visual representations, and cross-modal alignment features. These signals are fused via branch-wise evidence encoding and uncertainty-aware attention. We also extend the LLM-as-a-Judge paradigm to VQA hallucination and propose a low-cost strategy to automatically generate model-dependent supervision signals, enabling supervised training without costly human labels while maintaining high detection accuracy. Experiments on multiple VQA benchmarks show that FaithSCAN significantly outperforms existing methods in both effectiveness and efficiency. In-depth analysis shows hallucinations arise from systematic internal state variations in visual perception, cross-modal reasoning, and language decoding. Different internal signals provide complementary diagnostic cues, and hallucination patterns vary across VLM architectures, offering new insights into the underlying causes of multimodal hallucinations. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Hallucination Detection on HalLoc-VQA](): Hallucination Detection; HalLoc VQA; ROC: 77.9. - [Hallucination Detection on HaloQuest](): Hallucination Detection; HaloQuest; ROC: 0.829. - [Hallucination Detection on VQA v2](): Hallucination Detection; VQA v2; ROC: 0.752. ## Original sources and implementation [Original paper]() [PDF]()