MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models

Overview

This repo contains the source code of MMDT (Multimodal DecodingTrust). This research endeavor is designed to help researchers and practitioners better understand the capabilities, limitations, and potential risks involved in deploying these state-of-the-art Multimodal foundation models (MMFMs). See our paper for details.

This project is organized around the following six primary perspectives of trustworthiness, including:

1. Safety
2. Hallucination
3. Fairness
4. Privacy
5. Adversarial robustness
6. Out-of-Distribution Robustness

Project Structure

This project is structured around subdirectories dedicated to each area of trustworthiness. Each subdir includes scripts, data, and a dedicated README for easy comprehension.

Getting Started

Clone the repository

git clone https://github.com/AI-secure/MMDT.git && cd MMDT

Install requirements

conda create --name mmdt python=3.9
conda activate mmdt
pip install -r requirements.txt

Download MMDT dataset

We provide our dataset at this URL. Please unzip the file and put the folder at the repository root ./data. The directory will look like:

.
├── data
│   ├── image-to-text
│   └── text-to-image
├── mmdt
├── scripts
├── README.md
└── requirements.txt

Evaluate all perspectives

python mmdt/main.py --model_id {model_id}

Evaluate each perspective

To evaluate MMFMs with MMDT, we provide script examples for different perspectives under ./scripts. For example, to evaluate llava-hf/llava-v1.6-mistral-7b-hf on hallucination under natural selection scenario and action recognition task, we can run the following example script.

bash script/hallucination.sh

Notes

• Each of the six areas has its subdirectory containing the respective code and README.

• Follow the specific README: Every subdirectory has its own README. Refer to these documents for information on how to run the scripts and interpret the results.

License

This project is licensed under the CC BY 4.0 - see the LICENSE file for details.

Contact

Please reach out to us if you have any questions or suggestions. You can submit an issue or pull request, or send an email to chejian2@illinois.edu.

Thank you for your interest in MMDT. We hope our work will contribute to a more trustworthy, fair, and robust AI future.