We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response to recent advances in open-access image generation models that raise concerns about falsifying visual evidence. Existing forgery models, trained on datasets with full-image synthesis or large manipulated regions in object-centric images, struggle to generalise to surveillance scenarios. This is because tampering in surveillance imagery is typically localised and subtle, occurring in scenes with varied viewpoints, small or occluded subjects, and lower visual quality. To address this gap, SurFITR provides a large collection of forensically valuable imagery generated via a multimodal LLM-powered pipeline, enabling semantically aware, fine-grained editing across diverse surveillance scenes. It contains over 137k tampered images with varying resolutions and edit types, generated using multiple image editing models. Extensive experiments show that existing detectors degrade significantly on SurFITR, while training on SurFITR yields substantial improvements in both in-domain and cross-domain performance. SurFITR is publicly available on GitHub.

翻译：我们提出监控伪造图像测试域（SurFITR），这是一个用于监控风格图像伪造检测与定位的数据集。针对近期开放访问图像生成模型引发视觉证据伪造的担忧，该数据集应运而生。现有基于全图合成或目标中心图像大区域篡改数据集训练的伪造检测模型，难以泛化至监控场景。这是因为监控影像中的篡改通常呈现局部性与隐蔽性，常发生于包含多变视角、小或遮挡目标、低视觉质量的场景中。为弥合这一差距，SurFITR通过多模态大语言模型驱动的流水线，生成大量具有取证价值的图像，实现跨多样监控场景的语义感知细粒度编辑。该数据集包含超过13.7万张篡改图像，涵盖多种分辨率与编辑类型，由多个图像编辑模型生成。大量实验表明，现有检测器在SurFITR上性能显著下降，而在SurFITR上训练则可显著提升域内与跨域性能。SurFITR已在GitHub公开提供。