Datasets:

nebula
/

CocoGlide

pretty_name: CocoGlide
task_categories:
  - image-classification
  - image-segmentation
  - image-to-image
tags:
  - image-forgery-detection
  - image-manipulation-localization
  - synthetic-image-detection
  - diffusion-inpainting
  - GLIDE
  - COCO

CocoGlide Dataset Card

Dataset Description

This dataset is a reformatted version of CocoGlide, a local image forgery detection and localization dataset associated with the paper TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization.

CocoGlide is built from images in the MS-COCO validation set. The fake images are generated by applying GLIDE-based text-guided inpainting to local image regions. Each manipulated image is paired with a binary localization mask indicating the forged region.

The dataset can be used for both:

Image-level forgery detection, where the goal is to classify whether an image is real or fake.
Pixel-level forgery localization, where the goal is to predict the manipulated region.

Dataset Structure

Each sample contains the following fields:

Field	Type	Description
`key`	string	Sample identifier or image path
`image`	image	Input image
`mask`	image	Binary forgery localization mask
`label`	int	Image-level label, where `0` means real and `1` means fake
`prompt`	string or null	Text prompt/category used for GLIDE inpainting; `null` for real images

Label Definition

Label	Meaning
`0`	Real image
`1`	GLIDE-inpainted fake image

For real images, the corresponding mask should be treated as an all-zero mask.

Intended Use

This dataset is intended for research on:

Image forgery detection
Image manipulation localization
AI-generated image detection
Diffusion-based inpainting forgery detection
Robustness evaluation of forensic models

Typical evaluation settings include:

Image-level metrics: Accuracy, AUC, AP, F1
Pixel-level metrics: F1, IoU, AUC, localization AP

Source Paper

This dataset should be cited through the following paper:

@InProceedings{Guillaro_2023_CVPR,
  author    = {Guillaro, Fabrizio and Cozzolino, Davide and Sud, Avneesh and Dufour, Nicholas and Verdoliva, Luisa},
  title     = {TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {20606--20615}
}