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

• URL: http://arxiv.org/abs/2305.05784v1
• Date: Tue, 9 May 2023 22:09:35 GMT
• Title: Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools
• Abstract summary: 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.
• Score: 13.052016660119499
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Towards a satellite image manipulation and deepfake localization benchmark dataset [0.5774786149181393]
  verifying the authenticity of satellite imagery has become increasingly critical given advances in generative artificial intelligence.<n>The remote sensing community lacks high-quality, fine-grained manipulation datasets suitable for training and evaluating detection and image forensics algorithms.<n>We describe a preliminary dataset construction process and prototype benchmark dataset for satellite image manipulation detection and localization.
  arXiv  Detail & Related papers  (2026-08-05T13:36:42Z)
• Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations [56.816929931908824]
  We pioneer the detection of semantically-coordinated manipulations in multimodal data.<n>We propose a Retrieval-Augmented Manipulation Detection and Grounding (RamDG) framework.<n>Our framework significantly outperforms existing methods, achieving 2.06% higher detection accuracy on SAMM compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-16T04:18:48Z)
• DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images [21.22466658711056]
  We propose a novel method for auto-constructing datasets from real-world images by a multiagent collaborative system.<n>By coordinating four different agents equipped with Multi-modal Large Language Models (MLLMs), we are able to construct high-quality image datasets.<n>In particular, two types of experiments are conducted, including expanding existing datasets and creating new ones from scratch.
  arXiv  Detail & Related papers  (2025-07-11T14:51:33Z)
• LEGION: Learning to Ground and Explain for Synthetic Image Detection [49.958951540410816]
  We introduce SynthScars, a high-quality and diverse dataset consisting of 12,236 fully synthetic images with human-expert annotations. It features 4 distinct image content types, 3 categories of artifacts, and fine-grained annotations covering pixel-level segmentation, detailed textual explanations, and artifact category labels. We propose LEGION, a multimodal large language model (MLLM)-based image forgery analysis framework that integrates artifact detection, segmentation, and explanation.
  arXiv  Detail & Related papers  (2025-03-19T14:37:21Z)
• Low-Biased General Annotated Dataset Generation [62.04202037186855]
  We present a low-biased general annotated dataset generation framework (lbGen) Instead of expensive manual collection, we aim at directly generating low-biased images with category annotations. Experimental results confirm that, compared with the manually labeled dataset or other synthetic datasets, the utilization of our generated low-biased dataset leads to stable generalization capacity enhancement.
  arXiv  Detail & Related papers  (2024-12-14T13:28:40Z)
• Swin Transformer for Robust Differentiation of Real and Synthetic Images: Intra- and Inter-Dataset Analysis [0.0]
  This study proposes a Swin Transformer-based model for accurate differentiation between natural and synthetic images. The model's performance was evaluated through intra-dataset and inter-dataset testing across three distinct datasets.
  arXiv  Detail & Related papers  (2024-09-07T06:43:17Z)
• ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection [0.0]
  We introduce ImagiNet, a dataset of 200K examples spanning four categories: photos, paintings, faces, and miscellaneous. Synthetic images in ImagiNet are produced with both open-source and proprietary generators, whereas real counterparts for each content type are collected from public datasets.
  arXiv  Detail & Related papers  (2024-07-29T13:57:24Z)
• Diffusion Models as Data Mining Tools [87.77999285241219]
  This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. We show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease.
  arXiv  Detail & Related papers  (2024-07-20T17:14:31Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• JourneyDB: A Benchmark for Generative Image Understanding [89.02046606392382]
  We introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension.
  arXiv  Detail & Related papers  (2023-07-03T02:39:08Z)
• Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis [18.830738606514736]
  Deep learning methods are frequently hindered from deploying to real-world surgical applications. Data collection, annotation, and domain shift in-between sites and patients are the most common obstacles. We mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets.
  arXiv  Detail & Related papers  (2023-06-28T15:06:44Z)
• AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics [31.714342131823987]
  This paper aims to investigate the level of challenge that language-image generation models pose to media forensics. We propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt. This approach has resulted in the creation of a new image dataset called AutoSplice, containing 5,894 manipulated and authentic images.
  arXiv  Detail & Related papers  (2023-04-14T00:14:08Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data [58.50411487497146]
  We propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing.
  arXiv  Detail & Related papers  (2021-08-06T04:00:28Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming [3.4788711710826083]
  We propose an alternative solution with respect to the common data augmentation methods, applying it to the problem of crop/weed segmentation in precision farming. We create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. In addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images.
  arXiv  Detail & Related papers  (2020-09-12T08:49:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.