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Image Classifiers Leak Sensitive Attributes About Their Classes

Attack Concept

Abstract: Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy. To investigate this privacy leakage, we introduce the first Class Attribute Inference Attack (CAIA), which leverages recent advances in text-to-image synthesis to infer sensitive attributes of individual classes in a black-box setting, while remaining competitive with related white-box attacks. Our extensive experiments in the face recognition domain show that CAIA can accurately infer undisclosed sensitive attributes, such as an individual's hair color, gender, and racial appearance, which are not part of the training labels. Interestingly, we demonstrate that adversarial robust models are even more vulnerable to such privacy leakage than standard models, indicating that a trade-off between robustness and privacy exists.
Full Paper

Setup and Preparation

Setup Docker Container

The easiest way to perform our experiments is to run the code in a Docker container. To build the Docker image, run the following script:

docker build -t caia  .

Our Dockerfile also allows storing a Weights & Biases API key directly in the image. For this, provide the API key as an argument when building the image:

docker build -t caia --build-arg wandb_key=xxxxxxxxxx .

To create and start a Docker container, run the following command from the project's root:

docker run --rm --shm-size 16G --name caia_0 --gpus '"device=0"' -v $(pwd):/workspace -it caia bash

To add additional GPUs, modify the option '"device=0,1,2"' accordingly. Detach from the container using Ctrl+P followed by Ctrl+Q.

Setup Weights & Biases

We rely on Weights & Biases for experiment tracking and result storage. A free account is needed at wandb.ai to track the experiments. Note that we do not have any commercial relationship with Weights & Biases.

To connect an account to Weights & Biases, run the following command and add an API key (if not provided earlier in the docker build command):

wandb init

You can find the key at wandb.ai/settings. After the key was added, stop the script with Ctrl+C.

Prepare Training Datasets

We support FaceScrub and CelebA as datasets to train the target models. Please follow the instructions on the websites to download the individual datasets. Place all datasets in the folder data and make sure that the following structure is kept:

.
├── data       
    ├── celeba
        ├── img_align_celeba
        ├── identity_CelebA.txt
        ├── list_attr_celeba.txt
        ├── list_bbox_celeba.txt
        ├── list_eval_partition.txt
        ├── list_landmarks_align_celeba.txt
        └── list_landmarks_celeba.txt
    ├── facescrub
        ├── actors
            ├── faces
            └── images
        └── actresses
            ├── faces
            └── images

For CelebA, we used a custom crop of the images using the HD CelebA Cropper to increase the resolution of the cropped and aligned samples. We cropped the images using a face factor of 0.65 and resized them to size 224x224 with bicubic interpolation. The other parameters were left at default. To use another cropped or uncropped version, just replace the img_align_celeba folder with the alternative images and adjust the root attribute in the dataset setting in the training configuration file. For FaceScrub, faces contains the cropped images, images the original, uncropped images.

Training

Training Filter Models

To improve the attack sample generation process and filter out generated images that do not depict the target attribute values, we trained different filter models on CelebA on the attributes gender, hair_color, and eyeglasses. Training hyperparameters, augmentations, and WandB options are set in configuration files. To train,e.g., a gender classifier, run the following script:

python train_model.py -c=configs/training/filter_models/gender_classifier.yaml

We further utilized a pretrained FairFace classifier from github.com/dchen236/FairFace to filter the racial appearance images. Download the res34_fair_align_multi_4_20190809.pt model from Google Drive and put it into weights/fairface/res34_fair_align_multi_4_20190809.pt. If another directory is used to store the weights, please adjust the synthesizing configuration files accordingly.

Training Target Models

Our code currently allows training all ResNet, ResNeSt, DenseNet, and Inception-v3 models stated at pytorch.org/vision/stable/models. To add support for other models, modify the method _build_model in models/classifier.py, add the new model, and adjust its output layer's size.

To define the model and training configuration, create a configuration file. We provide an example configuration with explanations at configs/training/default_training.yaml. To train the target models accordingly to our paper, we provide a training configuration for each dataset. You only need to adjust the architecture used and the Weights & Biases configuration - all other parameters are identical for each target dataset. Only the batch size has to be adjusted for larger models.

After a training configuration file has been created, run the following command to start the training with the specified configuration:

python train_model.py -c=configs/training/{CONFIG}

Crafting Attack Dataset

To generate attack images that depict different values for the sensitive attribute to infer, two steps are required. First, existing images need to be inverted and encoded into the domain of diffusion models. For our experiments, we used samples from the FFHQ and CelebAHQ datasets. But other facial image datasets are also possible to use. Please put the images to invert into a folder and run the following script:

python invert_dataset.py -i={INPUT_FOLDER} -o={OUTPUT_FOLDER} -p='a photo of a person'

{INPUT_FOLDER} denotes the path to the images to invert. The inverted files are then stored in {OUTPUT_FOLDER}. Make sure to provide a suitable prompt -p to describe the general domain of the images. The rename the system process, add user initials with the -p option.

After the inversion is done, the attribute variations can be generated. For this, please adjust the provided configuration files in (configs/synthesizing)[configs/synthesizing]. Most importantly, provide the WandB run to the filter model with the required transformations, e.g., resizing and normalizing. Also adjust the image_latents value to the folder containing the inverted images. To add additional attribute variations, simply adjust the attribute_class and attributes values accordingly. To start the image generation process, run the following script and provide the path to the configuration file:

python generate_attribute_variations.py -c=configs/{CONFIG}

Synthesis Concept

Performing Class Attribute Inference Attacks

To perform our attacks, prepare an attack configuration file including the WandB run paths of the target and evaluation models. We provide an example configuration with explanations at configs/attacking/default_attacking.yaml. We further provide configuration files to reproduce the various attack results stated in our paper. You only need to adjust the WandB run paths for each dataset combination, and possibly the batch size.

After an attack configuration file has been created, run the following command to start the attack with the specified configuration:

python attack.py -c=configs/attacking/{CONFIG}

To run all attacks specified in a folder, here the resnet18 folder, run the following command:

for file in configs/attacking/non_robust/ffhq/gender/resnet18/*; do python attack.py -c=$file; done

All results including the metrics will be stored at WandB for easy tracking and comparison.

To perform attribute inference of Plug & Play Model Inversion Attacks (PPA), first perform PPA following the steps explained in the corresponding repository. After that, run the following script to infer the sensitive attributes:

python attack_ppa.py --filter_model={FILTER_MODEL} --ppa={PPA_RUNPATH} --labels=labels/celeba_subset_attributes/celeba500_gender_attribute_labels.csv --attribute='gender' --num_samples=25 --stylegan=stylegan2-ada-pytorch/ffhq.pkl

Replace {FILTER_MODEL} with the WandB run path of the filter model, or specify the path to the FairFace classifier. {PPA_RUNPATH} defines the WandB run path of the Model Inversion results. The --labels parameter points to the ground-truth attribute labels for the --attribute to infer. The --num_labels parameter defines how many synthetic samples per target have been computed by PPA. The value corresponds to the samples_per_target in their configuration files. We set the value to 25 in our experiments.

The attack further requires access to the StyleGAN2 model used for PPA. To set up StyleGAN2 and download the model weights, follow the instructions provided in the PPA repository. We also emphasize that StyleGAN2 is not compatible with our Dockerfile. Therefore, the simplest way is to use the PPA Dockerfile in this case.

Analyze Model Attribution

To analyze a model's attribution with Integrated Gradients, we used Captum in combination with a CelebAMask-HQ segmentation model. First, clone the CelebAMask-HQ repo and download the pretrained segmentation model. To then compute the relative attribution, run the following script:

python compute_model_attribution.py --classifier_runpath={CLASSIFIER_RUNPATH} --segmentation_model={SEGMENTATION_MODEL} --image_folder={IMAGE_FOLDER} --output_file=attribution.csv

Replace {CLASSIFIER_RUNPATH} with the WandB run path to the classifier model for which to compute the relative attribution. Set {SEGMENTATION_MODEL} to the local model path of the pretrained segmentation model. {IMAGE_FOLDER} specifies a path to a folder that contains the images used for the integrated gradient computation.

Attack Concept

Citation

If you build upon our work, please don't forget to cite us.

@inproceedings{struppek2023Caia,
    title={Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations},
    author={Lukas Struppek and Dominik Hintersdorf and Felix Friedrich and Manuel Brack and Patrick Schramowski and Kristian Kersting},
    booktitle = {Conference on Neural Information Processing Systems (NeurIPS) - Workshop on New Frontiers in Adversarial Machine Learning},
    year = {2024}
}

Implementation Credits

This repository uses https://github.com/LukasStruppek/Plug-and-Play-Attacks as its basis, particularly to train models and integrate WandB logging.

We also want to thank other authors for making their code publicly available, which supported our research in many ways. For license details, refer to the corresponding files in our repo. For more details on the specific functionality, please visit the corresponding repos.