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https://github.com/IBM/BadDiffusion
Official repo to reproduce the paper "How to Backdoor Diffusion Models?" published at CVPR 2023
https://github.com/IBM/BadDiffusion
Last synced: about 1 month ago
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Official repo to reproduce the paper "How to Backdoor Diffusion Models?" published at CVPR 2023
- Host: GitHub
- URL: https://github.com/IBM/BadDiffusion
- Owner: IBM
- License: apache-2.0
- Created: 2023-03-17T02:00:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-19T03:24:55.000Z (6 months ago)
- Last Synced: 2024-07-05T14:40:33.486Z (5 months ago)
- Language: Python
- Size: 2.07 MB
- Stars: 71
- Watchers: 6
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ai-security - BadDiffusion - _Official repo to reproduce the paper "How to Backdoor Diffusion Models?" published at CVPR 2023_ (Offensive tools and frameworks / Poisoning)
- awesome-ai-cybersecurity - BadDiffusion
- awesome-ai-cybersecurity - BadDiffusion
README
# BadDiffusion
Official repo to reproduce the paper "How to Backdoor Diffusion Models?" published at CVPR 2023
Paper link: https://arxiv.org/abs/2212.05400
## Environment
- Python 3.8.5
- PyTorch 1.10.1+cu11 or 1.11.0+cu102
## Usage
### Install Require Packages and Prepare Essential Data
Please run
```bash
bash install.sh
```
### Wandb Logging Support
If you want to upload the experimental results to ``Weight And Bias, please log in with the API key.
```bash
wandb login --relogin --cloud
```
### Prepare Dataset
#### Prepare Training Dataset
- CIFAR10: It will be downloaded by HuggingFace ``datasets`` automatically
- CelebA-HQ: Download the CelebA-HQ dataset and put the images under the folder ``./datasets/celeba_hq_256``
#### Prepare FID-Measuring Dataset
- CIFAR10: Create a folder, ``measure/CIFAR10``, and copy the CIFAR10 images (in ``.jpg`` format) into this folder before computing FID and MSE scores.
- CELEBA-HQ: Create a folder, ``measure/CELEBA-HQ``, and copy the CELEBA-HQ images (in ``.jpg`` format) into this folder before computing FID and MSE scores.
### Pre-Trained Models
I've uploaded all pre-trained backdoor diffusion models for [BadDiffusion](https://github.com/IBM/BadDiffusion) and [VillanDiffusion](https://github.com/IBM/VillanDiffusion) on [HuggingFace](https://huggingface.co/FrankCCCCC). Please feel free to download backdoored diffusion models from it.
### Run BadDiffusion
Arguments
- ``--project``: Project name for Wandb
- ``--mode``: Train or test the model, choice: 'train', 'resume', 'sampling`, 'measure', and 'train+measure'
- ``train``: Train the model
- ``resume``: Resume the training
- ``measure``: Compute the FID and MSE score for the BadDiffusion from the saved checkpoint, the ground truth samples will be saved under the 'measure' folder automatically to compute the FID score.
- ``train+measure``: Train the model and compute the FID and MSE score
- ``sampling``: Generate clean samples and backdoor targets from a saved checkpoint
- ``--dataset``: Training dataset, choice: 'MNIST', 'CIFAR10', and 'CELEBA-HQ'
- ``--batch``: Training batch size. Note that the batch size must be able to divide 128 for
the CIFAR10 dataset and 64 for the CelebA-HQ dataset.
- ``--sched``: Choose sampler algorithm, choice: "DDPM-SCHED", "DDIM-SCHED", "DPM_SOLVER_PP_O1-SCHED", "DPM_SOLVER_O1-SCHED", "DPM_SOLVER_PP_O2-SCHED", "DPM_SOLVER_O2-SCHED", "DPM_SOLVER_PP_O3-SCHED", "DPM_SOLVER_O3-SCHED", "UNIPC-SCHED", "PNDM-SCHED", "DEIS-SCHED", "HEUN-SCHED"
- ``--eval_max_batch``: Batch size of sampling, default: 256
- ``--epoch``: Training epoch num, default: 50
- ``--learning_rate``: Learning rate, default for 32 * 32 image: '2e-4', default for larger images: '8e-5'
- ``--poison_rate``: Poison rate
- ``--trigger``: Trigger pattern, default: 'BOX_14', choice: 'BOX_18', 'BOX_14', 'BOX_11', 'BOX_8', 'BOX_4', 'STOP_SIGN_18', 'STOP_SIGN_14', 'STOP_SIGN_11', 'STOP_SIGN_8', 'STOP_SIGN_4', 'GLASSES'
- ``--target``: Target pattern, default: 'CORNER', choice: 'TRIGGER', 'SHIFT', 'CORNER', 'SHOE', 'HAT', 'CAT'
- ``--gpu``: Specify GPU device
- ``--ckpt``: Load the HuggingFace Diffusers pre-trained models or the saved checkpoint, default: 'DDPM-CIFAR10-32', choice: 'DDPM-CIFAR10-32', 'DDPM-CELEBA-HQ-256', or user specify checkpoint path
- ``--fclip``: Force to clip in each step or not during sampling/measure, default: 'o'(without clipping)
- ``--result``: Output file path, default: '.'
For example, if we want to backdoor a DM pre-trained on CIFAR10 with **Grey Box** trigger and **Hat** target, we can use the following command
```bash
python baddiffusion.py --project default --mode train+measure --dataset CIFAR10 --batch 128 --epoch 50 --poison_rate 0.1 --trigger BOX_14 --target HAT --ckpt DDPM-CIFAR10-32 --fclip o -o --gpu 0
```
#### Training Backdoor Models & Measure the FID and MSE Scores
If we want to backdoor a DM pre-trained on Celeba-HQ with **GLASSES** trigger and **CAT** target, we can use the following command
```bash
python baddiffusion.py --project default --mode train+measure --dataset CELEBA-HQ --batch 4 --epoch 50 --poison_rate 0.1 --trigger GLASSES --target CAT --ckpt DDPM-CELEBA-HQ-256 --fclip o -o --gpu 0
```
#### Measure the FID and MSE Scores
If we want to measure the FID and MSE scores of a DM pre-trained on Celeba-HQ with **GLASSES** trigger and **CAT** target, we need to create a new folder ``measure/CIFAR10`` under this repository folder and copy the training images (in ``.jpg`` format) of CIFAR10 dataset into this folder. Then, we can use the following command
```bash
python baddiffusion.py --project default --mode measure --dataset CELEBA-HQ --eval_max_batch 256 --trigger GLASSES --target CAT --ckpt res_DDPM-CIFAR10-32_CIFAR10_ep50_c1.0_p0.1_BOX_14-HAT --fclip o -o --gpu 0
```
#### Generate Samples
If we want to generate the clean samples and backdoor targets from a backdoored DM, use the following command
Or simply generate the samples
```bash
python baddiffusion.py --project default --mode sampling --ckpt res_DDPM-CIFAR10-32_CIFAR10_ep50_c1.0_p0.1_BOX_14-HAT --fclip o --gpu 0
```
### Run Adversarial Neuron Pruning (ANP)
Arguments
- ``--project``: Project name for Wandb
- ``--epoch``: Training epoch num, default: 50
- ``--learning_rate``: Learning rate, default: '1e-4'
- ``--perturb_budget``: Perturbation budget, default: '4.0'
- ``--gpu``: Specify GPU device
- ``--ckpt``: Load the HuggingFace Diffusers pre-trained models or the saved checkpoint
- ``--output_dir``: Output file path, default: '.'
If we want to detect the Trojan of the backdoored model trained in the last section, we can use the following command
```bash
python anp_defense.py --project default --epoch 5 --learning_rate 1e-4 --perturb_budget 4.0 --ckpt res_DDPM-CIFAR10-32_CIFAR10_ep50_c1.0_p0.1_BOX_14-HAT --gpu 0
```