---
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
---

# SAMM: Semantic-Aligned Multimodal Manipulation Dataset

[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>

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

<div align="center">
<img src='./figures/samm_statistics.png' width='90%'>
</div>

## Annotations
```
    {
        "text": "Lachrymose Terri Butler, whose letter prompted Peter Dutton to cancel Troy Newman's visa, was clearly upset.",
        "fake_cls": "attribute_manipulation",
        "image": "emotion_jpg/65039.jpg",
        "id": 13,
        "fake_image_box": [
            665,
            249,
            999,
            671
        ],
        "cap_texts": {
            "Terri Butler": "Terri Butler Gender: Female, Occupation: Politician, Birth year: 1977, Main achievement: Member of Australian Parliament.",
            "Peter Dutton": "Peter Dutton Gender: Male, Occupation: Politician, Birth year: 1970, Main achievement: Australian Minister for Defence."
        },
        "cap_images": {
            "Terri Butler": "Terri Butler",
            "Peter Dutton": "Peter Dutton"
        },
        "idx_cap_texts": [
            1,
            0
        ],
        "idx_cap_images": [
            1,
            0
        ],
        "fake_text_pos": [
            0,
            11,
            13,
            14,
            15
        ]
    }
```

- `image`: The relative path to the original or manipulated image.  
- `text`: The original or manipulated text caption.  
- `fake_cls`: Indicates the type of manipulation (e.g., forgery, editing).  
- `fake_image_box`: The bounding box coordinates of the manipulated region in the image.  
- `fake_text_pos`: A list of indices specifying the positions of manipulated tokens within the `text` string.  
- `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).

## 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}
}
```