[2604.07101] SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.07101 (cs) [Submitted on 8 Apr 2026] Title:SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation Authors:Qizhou Wang, Guansong Pang, Christopher Leckie View a PDF of the paper titled SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation, by Qizhou Wang and 2 other authors View PDF HTML (experimental) Abstract: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 traini...