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MultiTrust is a comprehensive benchmark designed to assess and enhance the trustworthiness of MLLMs across five key dimensions: truthfulness, safety, robustness, fairness, and privacy. It integrates a rigorous evaluation strategy involving 32 diverse tasks to expose new trustworthiness challenges.

🚀 News

• 2024.09.26 🎉 Our paper has been accepted by NeurIPS 2024 Track Datasets and Benchmarks ！
• 2024.08.12 🌟 We released the latest results for DeepSeek-VL, and hunyuan-vision on our project website ！
• 2024.07.17 🌟 We released all data of MultiTrust ！You could use them for running all 32 tasks !
• 2024.07.07 🌟 We released the latest results for GPT-4o, Claude-3.5, and Phi-3 on our project website ！
• 2024.06.07 🌟 We released MultiTrust, the first comprehensive and unified benchmark on the trustworthiness of MLLMs !

🛠️ Installation

The envionment of this version has been updated to accommodate more latest models. If you want to ensure more precise replication of experimental results presented in the paper, you could switch to the branch v0.1.0.

• Option A: Pip install

  conda create -n multitrust python=3.9
  conda activate multitrust
  
  # Note: Tsinghua Source can be discarded.
  pip install -r env/requirements.txt
  pip install xformers==0.0.26.post1 --no-deps --index-url https://pypi.tuna.tsinghua.edu.cn/simple/
  pip install flash_attn==2.5.9.post1 --index-url https://pypi.tuna.tsinghua.edu.cn/simple/

• Option B: Docker

  • (Optional) Commands to install Docker

  # Our docker version:
  #     Client: Docker Engine - Community
  #     Version:           27.0.0-rc.1
  #     API version:       1.46
  #     Go version:        go1.21.11
  #     OS/Arch:           linux/amd64
  
  distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
  curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
  curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
  
  sudo apt-get update
  sudo apt-get install -y nvidia-container-toolkit
  
  sudo systemctl restart docker
  sudo usermod -aG docker [your_username_here]

  • Commands to install environment

  #  Note: 
  # [code] is an `absolute path` of project root: abspath(./)
  # [data] and [playground] are `absolute paths` of data and model_playground(decompress our provided data/playground).
  
  docker build -t multitrust:v0.0.1 -f env/Dockerfile .
  
  docker run -it \
      --name multitrust \
      --gpus all \
      --privileged=true \
      --shm-size=10gb \
      -v /home/[your_user_name_here]/.cache/huggingface:/root/.cache/huggingface \
      -v /home/[your_user_name_here]/.cache/torch:/root/.cache/torch \
      -v [code]:/root/multitrust \
      -v [data]:/root/multitrust/data \
      -v [playground]:/root/multitrust/playground \
      -w /root/multitrust \
      -p 11180:22 \
      -p 8000:8000 \
      -d multitrust:v0.0.1 /bin/bash
  
  # entering the container by docker exec
  docker exec -it multitrust /bin/bash
  
  # or entering by ssh
  ssh -p 11180 root@[your_ip_here]

• Several tasks require the use of commercial APIs for auxiliary testing. Therefore, if you want to test all tasks, please add the corresponding model API keys in env/apikey.yml.

✉️ Dataset

License

• The codebase is licensed under the CC BY-SA 4.0 license.

• MultiTrust is only used for academic research. Commercial use in any form is prohibited.

• If there is any infringement in MultiTrust, please directly raise an issue, and we will remove it immediately.

Data Preparation

Refer here for detailed instructions.

📚 Docs

Our document presents interface definitions for different modules and some tutorials on how to extend modules. Running online at: https://thu-ml.github.io/MMTrustEval/

Run following command to see the docs(locally).

mkdocs serve -f env/mkdocs.yml -a 0.0.0.0:8000

📈 Reproduce results in Our paper

Running scripts under scripts/run can generate the model outputs of specific tasks and corresponding primary evaluation results in either a global or sample-wise manner.

📌 To Make Inference

# Description: Run scripts require a model_id to run inference tasks.
# Usage: bash scripts/run/*/*.sh <model_id>

scripts/run
├── fairness_scripts
│   ├── f1-stereo-generation.sh
│   ├── f2-stereo-agreement.sh
│   ├── f3-stereo-classification.sh
│   ├── f3-stereo-topic-classification.sh
│   ├── f4-stereo-query.sh
│   ├── f5-vision-preference.sh
│   ├── f6-profession-pred.sh
│   └── f7-subjective-preference.sh
├── privacy_scripts
│   ├── p1-vispriv-recognition.sh
│   ├── p2-vqa-recognition-vispr.sh
│   ├── p3-infoflow.sh
│   ├── p4-pii-query.sh
│   ├── p5-visual-leakage.sh
│   └── p6-pii-leakage-in-conversation.sh
├── robustness_scripts
│   ├── r1-ood-artistic.sh
│   ├── r2-ood-sensor.sh
│   ├── r3-ood-text.sh
│   ├── r4-adversarial-untarget.sh
│   ├── r5-adversarial-target.sh
│   └── r6-adversarial-text.sh
├── safety_scripts
│   ├── s1-nsfw-image-description.sh
│   ├── s2-risk-identification.sh
│   ├── s3-toxic-content-generation.sh
│   ├── s4-typographic-jailbreaking.sh
│   ├── s5-multimodal-jailbreaking.sh
│   └── s6-crossmodal-jailbreaking.sh
└── truthfulness_scripts
    ├── t1-basic.sh
    ├── t2-advanced.sh
    ├── t3-instruction-enhancement.sh
    ├── t4-visual-assistance.sh
    ├── t5-text-misleading.sh
    ├── t6-visual-confusion.sh
    └── t7-visual-misleading.sh

📌 To Evaluate Results

After that, scripts under scripts/score can be used to calculate the statistical results based on the outputs and show the results reported in the paper.

# Description: Run scripts require a model_id to calculate statistical results.
# Usage: python scripts/score/*/*.py --model_id <model_id>

scripts/score
├── fairness
│   ├── f1-stereo-generation.py
│   ├── f2-stereo-agreement.py
│   ├── f3-stereo-classification.py
│   ├── f3-stereo-topic-classification.py
│   ├── f4-stereo-query.py
│   ├── f5-vision-preference.py
│   ├── f6-profession-pred.py
│   └── f7-subjective-preference.py
├── privacy
│   ├── p1-vispriv-recognition.py
│   ├── p2-vqa-recognition-vispr.py
│   ├── p3-infoflow.py
│   ├── p4-pii-query.py
│   ├── p5-visual-leakage.py
│   └── p6-pii-leakage-in-conversation.py
├── robustness
│   ├── r1-ood_artistic.py
│   ├── r2-ood_sensor.py
│   ├── r3-ood_text.py
│   ├── r4-adversarial_untarget.py
│   ├── r5-adversarial_target.py
│   └── r6-adversarial_text.py
├── safefy
│   ├── s1-nsfw-image-description.py
│   ├── s2-risk-identification.py
│   ├── s3-toxic-content-generation.py
│   ├── s4-typographic-jailbreaking.py
│   ├── s5-multimodal-jailbreaking.py
│   └── s6-crossmodal-jailbreaking.py
└── truthfulness
    ├── t1-basic.py
    ├── t2-advanced.py
    ├── t3-instruction-enhancement.py
    ├── t4-visual-assistance.py
    ├── t5-text-misleading.py
    ├── t6-visual-confusion.py
    └── t7-visual-misleading.py

📌 Task List

The total 32 tasks are listed here and ○: rule-based evaluation (e.g., keywords matching); ●: automatic evaluation by GPT-4 or other classifiers; ◐: mixture evaluation.

ID	Task Name	Metrics	Task Type	Eval
T.1	Basic World Understanding	Accuracy ($\uparrow$)	Dis.&Gen.	◐
T.2	Advanced Cognitive Inference	Accuracy ($\uparrow$)	Dis.	○
T.3	VQA under Instruction Enhancement	Accuracy ($\uparrow$)	Gen.	●
T.4	QA under Visual Assistance	Accuracy ($\uparrow$)	Gen.	●
T.5	Text Misleading VQA	Accuracy ($\uparrow$)	Gen.	●
T.6	Visual Confusion VQA	Accuracy ($\uparrow$)	Gen.	○
T.7	Visual Misleading QA	Accuracy ($\uparrow$)	Dis.	●
S.1	Risk Identification	Accuracy ($\uparrow$)	Dis.&Gen.	◐
S.2	Image Description	Toxicity Score ($\downarrow$), RtA ($\uparrow$)	Gen.	●
S.3	Toxicity Content Generation	Toxicity Score ($\downarrow$), RtA ($\uparrow$)	Gen.	◐
S.4	Plain Typographic Jailbreaking	ASR ($\downarrow$), RtA ($\uparrow$)	Gen.	◐
S.5	Optimized Multimodal Jailbreaking	ASR ($\downarrow$), RtA ($\uparrow$)	Gen.	◐
S.6	Cross-modal Influence on Jailbreaking	ASR ($\downarrow$), RtA ($\uparrow$)	Gen.	◐
R.1	VQA for Artistic Style images	Score ($\uparrow$)	Gen.	◐
R.2	VQA for Sensor Style images	Score ($\uparrow$)	Gen.	●
R.3	Sentiment Analysis for OOD texts	Accuracy ($\uparrow$)	Dis.	○
R.4	Image Captioning under Untarget attack	Accuracy ($\uparrow$)	Gen.	◐
R.5	Image Captioning under Target attack	Attack Success Rate ($\downarrow$)	Gen.	◐
R.6	Textual Adversarial Attack	Accuracy ($\uparrow$)	Dis.	○
F.1	Stereotype Content Detection	Containing Rate ($\downarrow$)	Gen.	●
F.2	Agreement on Stereotypes	Agreement Percentage ($\downarrow$)	Dis.	◐
F.3	Classification of Stereotypes	Accuracy ($\uparrow$)	Dis.	○
F.4	Stereotype Query Test	RtA ($\uparrow$)	Gen.	◐
F.5	Preference Selection in VQA	RtA ($\uparrow$)	Gen.	●
F.6	Profession Prediction	Pearson’s correlation ($\uparrow$)	Gen.	◐
F.7	Preference Selection in QA	RtA ($\uparrow$)	Gen.	●
P.1	Visual Privacy Recognition	Accuracy, F1 ($\uparrow$)	Dis.	○
P.2	Privacy-sensitive QA Recognition	Accuracy, F1 ($\uparrow$)	Dis.	○
P.3	InfoFlow Expectation	Pearson's Correlation ($\uparrow$)	Gen.	○
P.4	PII Query with Visual Cues	RtA ($\uparrow$)	Gen.	◐
P.5	Privacy Leakage in Vision	RtA ($\uparrow$), Accuracy ($\uparrow$)	Gen.	◐
P.6	PII Leakage in Conversations	RtA ($\uparrow$)	Gen.	◐

⚛️ Overall Results

• Proprietary models like GPT-4V and Claude3 demonstrate consistently top performance due to enhancements in alignment and safety filters compared with open-source models.
• A global analysis reveals a correlation coefficient of 0.60 between general capabilities and trustworthiness of MLLMs, indicating that more powerful general abilities could help better trustworthiness to some extent.
• Finer correlation analysis shows no significant link across different aspects of trustworthiness, highlighting the need for comprehensive aspect division and identifying gaps in achieving trustworthiness.

✒️ Citation

If you find our work helpful for your research, please consider citing our work.

@misc{zhang2024benchmarking,
      title={Benchmarking Trustworthiness of Multimodal Large Language Models: A Comprehensive Study}, 
      author={Yichi Zhang and Yao Huang and Yitong Sun and Chang Liu and Zhe Zhao and Zhengwei Fang and
              Yifan Wang and Huanran Chen and Xiao Yang and Xingxing Wei and Hang Su and Yinpeng Dong and
              Jun Zhu},
      year={2024},
      eprint={2406.07057},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }