Repository with code to reproduce the results for Bilateral Effects of Backdoored Diffusion

While state-of-the-art diffusion models (DMs) excel in image generation, concerns regarding their security persist. Earlier research highlighted DMs' vulnerability to backdoor attacks, but these studies placed stricter requirements than conventional methods like 'BadNets' in image classification. This is because the former necessitates modifications to the diffusion sampling and training procedures. Unlike the prior work, we investigate whether generating backdoor attacks in DMs can be as simple as BadNets, i.e., by only contaminating the training dataset without tampering the original diffusion process. In this more realistic backdoor setting, we uncover bilateral backdoor effects that not only serve an adversarial purpose (compromising the functionality of DMs) but also offer a defensive advantage (which can be leveraged for backdoor defense). Specifically, we find that a BadNets-like backdoor attack remains effective in DMs for producing incorrect images (misaligned with the intended text conditions), and thereby yielding incorrect predictions when DMs are used as classifiers. Meanwhile, backdoored DMs exhibit an increased ratio of backdoor triggers, a phenomenon we refer to as 'trigger amplification', among the generated images. We show that this latter insight can be used to enhance the detection of backdoor-poisoned training data. Even under a low backdoor poisoning ratio, studying the backdoor effects of DMs is also valuable for designing anti-backdoor image classifiers. Last but not least, we establish a meaningful linkage between backdoor attacks and the phenomenon of data replications by exploring DMs' inherent data memorization tendencies.

The DDPM part is adapted from Classifier-free Diffusion Guidance.

The Stable-Diffusion part is based on stable-diffusion-1 which makes fine-tuning Stable-Diffusion model easier.

cd stable-diffusion
pip install -r requirements.txt

1. ImageNette

Get the full size version of imagenette from here and unzip to "data/imagenette",

cd data/imagenette
wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz
tar -zxvf imagenette2.tgz

and produce the Badnets-like version.

python badnets_imagenette.py

2. Caltech15

Get the 15 classes subset of Caltech256 from here and unzip to "data/caltech",

cd data/caltech
wget https://drive.google.com/file/d/1Li9UAy6QyNDRGP_CAhNrGCAfocxjED3-/view?usp=sharing

and produce the Badnets-like version.

python badnets_caltech15.py

The pretrained stable diffusion model can be downloaded from huggingface.

cd stable-diffusion 
bash scripts/train.sh

SD model will be saved at logs/imagenette/experiment_name/checkpoints

cd stable-diffusion 
bash scripts/sample.sh

Samples model will be saved at outputs/imagenette/experiment_name

1. train classifier on ImageNette

cd classifier
bash scripts/train_imagenette.sh

This step trains a clean classifier (ResNet-50) on ImageNette and a backdoored classifier on the Badnets-like poisoned ImageNette. They are saved at model_ckpt/imagenette

2. run clean classifier on generated data.

cd classifier
python eval_imagenette.py

The generated prediction will be saved at stable-diffusion/result_clf/imagenette

3. eval trigger ratio and the ratio of generations mismatching their prompts

cd stable-diffusion 
python eval_tgr_imagenette.py

cd classifier
bash scripts/train_on_gen_imagenette.sh

Results save to classifier/result/imagenette_on_gen

cd ddpm
conda env create -f environment.yml

cd edm 
conda env create -f environment.yml -n edm

Generate backdoored cifar10 by CognitiveDistillation, save to .npz file. Then transform to .zip format by

bash scripts/data.sh

bash scripts/train.sh
bash scripts/pred.sh