Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools

Abstract

We present a first of its kind dataset of overhead imagery for development and evaluation of forensic tools. Our dataset consists of real, fully synthetic and partially manipulated overhead imagery generated from a custom diffusion model trained on two sets of different zoom levels and on two sources of pristine data. We developed our model to support controllable generation of multiple manipulation categories including fully synthetic imagery conditioned on real and generated base maps, and location. We also support partial in-painted imagery with same conditioning options and with several types of manipulated content. The data consist of raw images and ground truth annotations describing the manipulation parameters. We also report benchmark performance on several tasks supported by our dataset including detection of fully and partially manipulated imagery, manipulation localization and classification.

References in corpus (11)

• Denoising Diffusion Probabilistic Models
• Learning Transferable Visual Models From Natural Language Supervision
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Diffusion Models Beat GANs on Image Synthesis
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• RePaint: Inpainting using Denoising Diffusion Probabilistic Models
• GAN-generated Faces Detection: A Survey and New Perspectives
• On the detection of synthetic images generated by diffusion models
• An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images