Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: key
-    dtype: string
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: label
-    dtype: int32
-  - name: prompt
-    dtype: string
-  splits:
-  - name: glide
-    num_bytes: 122829264.128
-    num_examples: 1024
-  download_size: 122982932
-  dataset_size: 122829264.128
-configs:
-- config_name: default
-  data_files:
-  - split: glide
-    path: data/glide-*
+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}
+}