ImageProtector

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Repository for the Paper: Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection

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# ImageProtector

## Instructions for reimplementing ImageProtector

### 1. Install the required packages

```bash
git clone https://github.com/Sadcardation/ImageProtector.git
cd ImageProtector
conda env create -f environment.yml
conda activate mllm_refusal
```
- Oct 16, 2024: Because many libraries have been updated, and running above commands maybe not prepare the environment correctly for this project, we recommend preparing separate environments for each MLLM according to their instructions and installing necessary libraries accordingly. The libraries for a unified environment are listed in `requirements.txt`.

### 2. Prepare the datasets

Check the datasets from the following links:

- [**CelebA**](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html): [Download Link](https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg&usp=drive_link) (Validation)
- [**GQA**](https://cs.stanford.edu/people/dorarad/gqa/about.html): [Download Link](https://cs.stanford.edu/people/dorarad/gqa/download.html) (Test Balanced)
- [**TextVQA**](https://textvqa.org/): [Download Link](https://textvqa.org/dataset/) (Test)
- [**VQAv2**](https://visualqa.org/): [Download Link](https://visualqa.org/download.html) (Validation)

Download the datasets and place them in the `datasets` directory. The directory structure should look like this:

```
ImageProtector
└── datasets
    ├── CelebA
    │   ├── Images
    │   │   ├── 166872.jpg
    │   │   └── ...
    │   ├── sampled_data_100.xlsx
    │   └── similar_questions.json
    ├── GQA
    │   ├── Images
    │   │   ├── n179334.jpg
    │   │   └── ...
    │   ├── sampled_data_100.xlsx
    │   └── similar_questions.json
    ├── TextVQA
    │   ├── Images
    │   │   ├── 6a45a745afb68f73.jpg
    │   │   └── ...
    │   ├── sampled_data_100.xlsx
    │   └── similar_questions.json
    └── VQAv2
        ├── Images
        │   └── mscoco
        │       └── val2014
        │           ├── COCO_val2014_000000000042.jpg
        │           └── ...
        ├── sampled_data_100.xlsx
        └── similar_questions.json   
```

`sampled_data_100.xlsx` contains the 100 sampled image-question for each dataset. `similar_questions.json` contains the similar questions for each questions in the sampled data.

### 3. Prepare the MLLMs

Clone the MLLM repositories and place them in the `models` directory, and follow the install instructions for each MLLM. Include corresponding `utils` directory in each MLLM's directory.

- [**LLaVA-1.5**](https://github.com/haotian-liu/LLaVA)

Additional instructions:

1. Add
     ```
     config.mm_vision_tower = "openai/clip-vit-large-patch14"
     ```

below [here](https://github.com/haotian-liu/LLaVA/blob/c121f0432da27facab705978f83c4ada465e46fd/llava/model/language_model/llava_llama.py#L44) to replace original vision encoder `openai/clip-vit-large-patch14-336` LLaVA uses to unify resolutions of perturbed images between different MLLMs.
  2. Comment all `@torch.no_grad()` for `forward` related function in image encoder modeling file [clip_encoder.py](https://github.com/haotian-liu/LLaVA/blob/c121f0432da27facab705978f83c4ada465e46fd/llava/model/multimodal_encoder/clip_encoder.py)
- [**MiniGPT-4**](https://github.com/Vision-CAIR/MiniGPT-4)
- [**InstructBLIP**](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip)
- [**Qwen-VL-Chat**](https://github.com/QwenLM/Qwen-VL)

Additional instructions:

1. Add

```python
     if kwargs:
         kwargs['visual']['image_size'] = 224
     ```

below [here](https://huggingface.co/Qwen/Qwen-VL-Chat/blob/f57cfbd358cb56b710d963669ad1bcfb44cdcdd8/configuration_qwen.py#L61) to unify resolutions of perturbed images between different MLLMs.
  2. Add

```python
     image_emb = None,
     ```

as addtional [argument for forward function of QWenModel](https://huggingface.co/Qwen/Qwen-VL-Chat/blob/f57cfbd358cb56b710d963669ad1bcfb44cdcdd8/modeling_qwen.py#L538-L553), and replace [this line of code](https://huggingface.co/Qwen/Qwen-VL-Chat/blob/f57cfbd358cb56b710d963669ad1bcfb44cdcdd8/modeling_qwen.py#L565) with

```python
     images = image_emb if image_emb is not None else self.visual.encode(images)
     ```

so that image embeddings can directly be passed to the forward function.

### 4. Run the experiments

To produced images with refusal perturbation on 100 sampled images for VQAv2 dataset on LLaVA-1.5 with three different types of shadow questions under default settings, run the following command:

```bash
./attack.sh
```

The results will be saved under LLaVA-1.5's directory.

### 5. Evaluate the results

To evaluate the results, run the following command:

```bash
./evaluate.sh
```

with corresponding MLLM's directory and the name of the result directory. Refusal Rates will be printed on the terminal and saved in the each result directory.

## Citation

If you find ImageProtector helpful in your research, please consider citing:

```bibtex
@article{shao2026leave,
  title={Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection},
  author={Shao, Zedian and Liu, Hongbin and Hu, Yuepeng and Gong, Neil Zhenqiang},
  journal={arXiv preprint arXiv:2604.09024},
  year={2026}
}
```

## Acknowledgement

- [Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs](https://github.com/ebagdasa/multimodal_injection)
- [Visual Adversarial Examples Jailbreak Aligned Large Language Models](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models)

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