HalluGen：合成逼真可控的幻觉以评估图像复原
HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restoration
HalluGen 提供了一种可控合成幻觉的扩散框架，可用于生成带有类型、位置和严重程度标注的幻觉数据，进而训练无参考幻觉检测器或评估图像质量指标；其提出的 SHAFE 指标利用特征和软注意力池化提升幻觉敏感性，对图像复原任务的可信度评估有直接借鉴意义。
原文依据 We introduce HalluGen, a diffusion-based framework that synthesizes realistic hallucinations with controllable type, location, and severity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论生成模型容易产生幻觉：看似合理但实际不存在于真实标注中的错误结构。这一问题在医学影像、工业检测和遥感等安全关键领域的图像复原中尤为突出，因为此类错误会削弱可靠性和信任度。例如，在资源有限地区广泛使用的低场MRI中，复原模型对于增强低质量扫描至关重要，但幻觉可能导致严重的诊断错误。进展一直受到循环依赖的阻碍：评估幻觉需要标注数据，而此类标注成本高昂且主观。我们提出HalluGen，一个基于扩散的框架，能够合成具有可控类型、位置和严重程度的逼真幻觉，生成感知上逼真但语义上错误的输出（分割IoU从0.86降至0.36）。利用HalluGen，我们构建了首个大规模幻觉数据集，包含来自1450张脑部MR图像的4350张标注图像，用于低场增强，从而能够系统评估幻觉检测与缓解。我们展示了其在两个应用中的效用：（1）对图像质量指标进行基准测试，并开发了基于特征评估的语义幻觉评估（SHAFE），一种基于特征且带有软注意力池化的指标，相比传统指标提升了幻觉敏感性；（2）训练无参考幻觉检测器，能够泛化到真实的复原失败案例。HalluGen及其开放数据集共同为安全关键图像复原中的幻觉评估建立了首个可扩展的基础。
英文原文摘要 1671 字符
Generative models are prone to hallucinations: plausible but incorrect structures absent in the ground truth. This issue is problematic in image restoration for safety-critical domains such as medical imaging, industrial inspection, and remote sensing, where such errors undermine reliability and trust. For example, in low-field MRI, widely used in resource-limited settings, restoration models are essential for enhancing low-quality scans, yet hallucinations can lead to serious diagnostic errors.Progress has been hindered by a circular dependency: evaluating hallucinations requires labeled data, yet such labels are costly and subjective.We introduce HalluGen, a diffusion-based framework that synthesizes realistic hallucinations with controllable type, location, and severity, producing perceptually realistic but semantically incorrect outputs (segmentation IoU drops from 0.86 to 0.36).Using HalluGen, we construct the first large-scale hallucination dataset comprising 4,350 annotated images derived from 1,450 brain MR images for low-field enhancement, enabling systematic evaluation of hallucination detection and mitigation.We demonstrate its utility in two applications: (1) benchmarking image quality metrics and developing Semantic Hallucination Assessment via Feature Evaluation (SHAFE), a feature-based metric with soft-attention pooling that improves hallucination sensitivity over traditional metrics; and (2) training reference-free hallucination detectors that generalize to real restoration failures.Together, HalluGen and its open dataset establish the first scalable foundation for evaluating hallucinations in safety-critical image restoration.