fanghenshaometeor/circular-teaching

Codes for circular-teaching [working in progress...]

On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness

This is the official code repo for the paper entitled On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness.

SPACTE in a nutshell

The proposed SPACTE in our paper is an efficient and effective certifiably-robust defense. SPACTE augments an individual DNN with multiple heads, each of which pertains a classifier for the ensemble. Particularly, SPACTE enables a circular communication flow among those augmented heads, i.e., each head teaches its neighbor with the self-paced learning using smoothed losses, which are specifically designed in relation to certified robustness.

The deployed multi-head structure and the circular-teaching scheme of SPACTE jointly contribute to diversify and enhance the classifiers in augmented heads for ensemble, leading to even stronger certified robustness than ensembling multiple DNNs (effectiveness) at the cost of much less computational expenses (efficiency).

Overview of the Repo

SPACTE is compatible with all single-model-based certified defenses, among which 3 representative defenses are selected:

• Gaussian (ICML'19): the pioneering work on randomized-smoothing. [paper, code]
• Consistency (NeurIPS'20): the SOTA regularization-based certified defense. [paper, code]
• SmoothMix (NeurIPS'21): the SOTA data-augmentation-based certified defense. [paper, code]

Accordingly, this repo is organized based on the 3 defenses.

• code/ contains the code for training SPACTE with base methods Gaussian (code/0Gaussian/), Consistency (code/1Consistency/) and SmoothMix (code/2SmoothMix/), respectively.
• logs_certification/ provides the corresponding certification log data of our SPACTE models.

For example, take a close look at the files in code/0Gaussian/:

1. model/*_mhead.py: model definitions of the multi-head DNN
2. circular_teaching.py: main codes of SPACTE
3. core_mhead.py: smoothed classifier of the multi-head DNN
4. certify_mhead.py: certification script
5. train.py: training script
6. utils.py: utility functions

Getting started

Install dependencies

conda create -n spacte python=3.6
conda activate spacte
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch # for Linux
pip install pandas, scipy, statsmodels, tensorboard

Examples

Let us see an example on how to train and certifiy a 5-head ResNet-110 via SPACTE upon the Gaussian baseline on CIFAR10 with a noise level $\sigma=0.25$.

Training and certification

cd Circular-teaching
cd code/0Gaussian
# training
CUDA_VISIBLE_DEVICES=0 python train.py --arch R110 --dataset CIFAR10 --data_dir $data_dir$ --noise_sd 0.25 --num_heads 5 --num_noise_vec 2 --lbdlast 1.2
# certification
CUDA_VISIBLE_DEVICES=0 python certify_mhead.py --arch R110 --dataset CIFAR10 --data_dir $data_dir$ --model_path './save/CIFAR10/R110/noise-0.25/h-5-eps-0.8-m-2-lbdlast-1.2/epoch150.pth' --noise_sd 0.25 --skip 20 --num_heads 5

• By running the train.py script, the models are by default saved in the created folder ./save/CIFAR10/R110/noise-0.25/h-5-eps-0.8-m-2-lbdlast-1.2/. In addition, two folders, ./logs/ and ./runs/, are also created to record the training information.
• By running the certify_mhead.py script, the corresponding logs are by default saved in the created folder ./logs/CIFAR10/R110/noise-0.25/certify-mhead/, including the detailed certified radius of each sample, the ACR and the approximated certified accuracy at given radii.

A full collection of all the training and certification commands can be found in EXPERIMENTS.md, where the detailed values of all the involved hyper-parameters in experiments are presented.

SPACTE-trained models

Our trained models of SPACTE will be released soon.

Additional references

We list below the 2 certifiably-robust ensemble defenses and other 2 single-model-based certified defenses discussed in our paper.

• Horváth et al. (ICLR'22): naive ensemble of multiple DNNs trained from different random seeds. [paper, code]
• Yang et al. (ICLR'22): fine-tuning multiple well-pretrained models via diversified gradient and large confidence margin. [paper, code]
• MACER (ICLR'20): regularization-based by directly maximizing the certified radius. [paper, code]
• SmoothAdv (NeurIPS'19): data-augmentation-based by involving the adversarial examples of the smoothed classifier into training. [paper, code]