Improve dataset card: Add paper/code links, update task categories, add sample usage and citation

README.md

@@ -1,36 +1,54 @@
 ---
-license: apache-2.0
-task_categories:
-- token-classification
-- text-classification
 language:
 - en
+license: apache-2.0
 size_categories:
 - 100K<n<1M
+task_categories:
+- token-classification
+- text-classification
+- image-text-to-text
+- object-detection
+tags:
+- multimodal
+- manipulation-detection
+- media-forensics
+- deepfake-detection
 ---
 
-# Notes ⚠️
- 
-- If you want to import the CAP data into your own dataset, please refer to [this](https://github.com/shen8424/CAP).
-- If you want to run RamDG on datasets other than SAMM and use CNCL to incorporate external knowledge, please ensure to configure ```idx_cap_texts``` and ```idx_cap_images``` in the dataset jsons.
-- We have upgraded the SAMM JSON files. The latest versions (SAMM with CAP or without CAP) are available on July 24, 2025. Please download the newest version.
+# SAMM: Semantic-Aligned Multimodal Manipulation Dataset
 
-# Brief introduction
+[Paper](https://huggingface.co/papers/2509.12653) | [Code](https://github.com/shen8424/SAMM-RamDG-CAP)
+
+## Introduction
+
+The detection and grounding of manipulated content in multimodal data has emerged as a critical challenge in media forensics. While existing benchmarks demonstrate technical progress, they suffer from misalignment artifacts that poorly reflect real-world manipulation patterns: practical attacks typically maintain semantic consistency across modalities, whereas current datasets artificially disrupt cross-modal alignment, creating easily detectable anomalies. To bridge this gap, we pioneer the detection of semantically-coordinated manipulations where visual edits are systematically paired with semantically consistent textual descriptions. Our approach begins with constructing the first Semantic-Aligned Multimodal Manipulation (SAMM) dataset.
+
+We present **SAMM**, a large-scale dataset for Detecting and Grounding Semantic-Coordinated Multimodal Manipulation. This is the official implementation of *SAMM* and *RamDG*. We propose a realistic research scenario: detecting and grounding semantic-coordinated multimodal manipulations, and introduce a new dataset SAMM. To address this challenge, we design the RamDG framework, proposing a novel approach for detecting fake news by leveraging external knowledge.
 
 <div align="center">
 <img src='./figures/teaser.png' width='90%'>
 </div>
 
-We present <b>SAMM</b>, a large-scale dataset for Detecting and Grounding Semantic-Coordinated Multimodal Manipulation. The official code has been released at [this](https://github.com/shen8424/SAMM-RamDG-CAP).
+The framework of the proposed RamDG:
+
+<div align="center">
+<img src='https://github.com/shen8424/SAMM-RamDG-CAP/blob/main/figures/RamDG.png?raw=true' width='90%'>
+</div>
+
+## Notes ⚠️
+ 
+- If you want to import the CAP data into your own dataset, please refer to [this](https://github.com/shen8424/CAP).
+- If you want to run RamDG on datasets other than SAMM and use CNCL to incorporate external knowledge, please ensure to configure ```idx_cap_texts``` and ```idx_cap_images``` in the dataset jsons.
+- We have upgraded the SAMM JSON files. The latest versions (SAMM with CAP or without CAP) are available on July 24, 2025. Please download the newest version.
 
-**Dataset Statistics:**
+## Dataset Statistics
 
 <div align="center">
 <img src='./figures/samm_statistics.png' width='90%'>
 </div>
 
-
-# Annotations
+## Annotations
 ```
     {
         "text": "Lachrymose Terri Butler, whose letter prompted Peter Dutton to cancel Troy Newman's visa, was clearly upset.",
@@ -77,4 +95,104 @@ We present <b>SAMM</b>, a large-scale dataset for Detecting and Grounding Semant
 - `cap_texts`: Textual information extracted from CAP (Contextual Auxiliary Prompt) annotations.  
 - `cap_images`: Relative paths to visual information from CAP annotations.  
 - `idx_cap_texts`: A binary array where the i-th element indicates whether the i-th celebrity in `cap_texts` is tampered (1 = tampered, 0 = not tampered).  
-- `idx_cap_images`: A binary array where the i-th element indicates whether the i-th celebrity in `cap_images` is tampered (1 = tampered, 0 = not tampered).
+- `idx_cap_images`: A binary array where the i-th element indicates whether the i-th celebrity in `cap_images` is tampered (1 = tampered, 0 = not tampered).
+
+## Sample Usage (Training and Testing RamDG)
+
+The following snippets are taken from the official GitHub repository to demonstrate how to train and test the RamDG framework using this dataset.
+
+### Dependencies and Installation
+```bash
+mkdir code
+cd code
+git clone https://github.com/shen8424/SAMM-RamDG-CAP.git
+cd SAMM-RamDG-CAP
+conda create -n RamDG python=3.8
+conda activate RamDG
+conda install --yes -c pytorch pytorch=1.10.0 torchvision==0.11.1 cudatoolkit=11.3
+pip install -r requirements.txt
+conda install -c conda-forge ruamel_yaml
+```
+
+### Prepare Checkpoint
+
+Download the pre-trained model through this link: [ALBEF_4M.pth](https://storage.googleapis.com/sfr-pcl-data-research/ALBEF/ALBEF_4M.pth) and [pytorch_model.bin](https://drive.google.com/file/d/15qfsTHPB-CkEVreOyf-056JWDAVjWK3w/view?usp=sharing)[GoogleDrive].
+
+Then put the `ALBEF_4M.pth` and `pytorch_model.bin` into `./code/SAMM-RamDG-CAP/`.
+
+```
+./
+├── code
+    └── SAMM-RamDG-CAP (this github repo)
+        ├── configs
+        │   └──...
+        ├── dataset
+        │   └──...
+        ├── models
+        │   └──...
+        ...
+        └── ALBEF_4M.pth
+        └── pytorch_model.bin
+```
+
+### Prepare Data
+
+We provide two versions: SAMM with CAP information and SAMM without CAP information. If you choose SAMM with CAP information, download `people_imgs1` and `people_imgs2`, then move the data from both folders to `./code/SAMM-RamDG-CAP/SAMM_datasets/people_imgs`.
+
+Then place the `train.json`, `val.json`, `test.json` into `./code/SAMM-RamDG-CAP/SAMM_datasets/jsons` and place `emotion_jpg`, `orig_output`, `swap_jpg` into `./code/SAMM-RamDG-CAP/SAMM_datasets`.
+
+```
+./
+├── code
+    └── SAMM-RamDG-CAP (this github repo)
+        ├── configs
+        │   └──...
+        ├── dataset
+        │   └──...
+        ├── models
+        │   └──...
+        ...
+        └── SAMM_datasets
+        │       ├── jsons
+        │       │   ├──train.json
+        │       │   │
+        │       │   ├──test.json
+        │       │   │
+        │       │   └──val.json
+        │       ├── people_imgs
+        │       │   ├──Messi (from people_imgs1)
+        │       │   ├──Trump (from people_imgs2)
+        │       │   └──... 
+        │       │
+        │       ├── emotion_jpg
+        │       │
+        │       ├── orig_output
+        │       │
+        │       ├── swap_jpg
+        ├── models
+        │   
+        └── pytorch_model.bin
+```
+
+### Training RamDG
+To train RamDG on the SAMM dataset, please modify `train.sh` and then run the following commands:
+```bash
+bash train.sh
+```
+
+### Testing RamDG
+To test RamDG on the SAMM dataset, please modify `test.sh` and then run the following commands:
+```bash
+bash test.sh
+```
+
+## Citation
+If you find this work useful for your research, please kindly cite our paper:
+```bibtex
+@inproceedings{shen2025beyond,
+  title={Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations},
+  author={Shen, Jinjie and Wang, Yaxiong and Chen, Lechao and Nan, Pu and Zhong, Zhun},
+  booktitle={ACM Multimedia},
+  year={2025}
+}
+```