| --- |
| 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: |
|
|
| ```bibtex |
| @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} |
| } |
