CROWN-IBP

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Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).

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CROWN-IBP: Towards Stable and Efficient Training of Verifiably Robust Neural Networks
========================

CROWN-IBP is a *certified defense* to adversarial examples, which combines a
tight linear relaxation based verification bound
([CROWN](http://arxiv.org/pdf/1811.00866.pdf)) with the efficient interval bound propagation
([IBP](https://arxiv.org/pdf/1810.12715.pdf)) training method.  We achieved
state-of-the-art *verified* (certified) error on MNIST and CIFAR: for MNIST,
**6.68\%** at `epsilon=0.3` and **12.46\%** at `epsilon=0.4` (L\_infinity norm
distortion); and for CIFAR, **67.11\%** at `epsilon=8/255` and **45.50\%** at
`epsilon=2/255`. The MNIST verified error is even lower than the unverified PGD
error (around 12% at `epsilon=0.3`) by 
[(Madry et al.)](https://github.com/MadryLab/mnist_challenge). More empirical
results and algorithm details of CROWN-IBP can be found in our paper:

*Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, and Cho-Jui Hsieh, ["Towards Stable and Efficient Training of Verifiably Robust Neural Networks"](https://openreview.net/pdf?id=Skxuk1rFwB), ICLR 2020*

Our repository provides high quality PyTorch implementations of
[IBP](https://github.com/deepmind/interval-bound-propagation) (Gowal et al.,
2018), CROWN-IBP (our algorithm), [Convex Adversarial
Polytope](https://github.com/locuslab/convex_adversarial) (Wong et al., 2018)
and (ordinary) [CROWN](https://github.com/huanzhang12/RecurJac-and-CROWN)
(Zhang et al., 2018) with **Multi-GPU** support.

If you want to apply CROWN-IBP to more general NN architectures (e.g.,
**ResNet/DenseNet**, **LSTM** and **Transformer**), use the new implementation
based on the [auto_LiRPA library](https://github.com/KaidiXu/auto_LiRPA).
`auto_LiRPA` is a powerful tool for computing linear relaxation based
perturbation analysis (e.g., CROWN) for general computational graphs.

We recently proposed a new technique, *loss fusion*, which further improves the
efficiency of CROWN-IBP. We can now use the tight CROWN-IBP bounds for training
at the same asympototic complexity as IBP, and scale to large datasets
including downscaled **ImageNet**. More details:
[https://arxiv.org/pdf/2002.12920.pdf](https://arxiv.org/pdf/2002.12920.pdf)

A TensorFlow implementation of CROWN-IBP is provided by [DeepMind](https://github.com/deepmind/interval-bound-propagation).

News
---------
* Apr 24, 2020: A new implementation of CROWN-IBP on more neural network
  architectures (**ResNet**, **LSTM**, **Transformer**) can be found in the
  [auto_LiRPA library](https://github.com/KaidiXu/auto_LiRPA).
* Nov 21, 2019: We released [pretrained SOTA
  models](https://download.huan-zhang.com/models/crown-ibp/models_crown-ibp_dm-large.tar.gz).
  On MNIST we can achieve
  6.68% verified error at `epsilon=0.3` and on CIFAR we can achieve 67.11%
  verified error at `epsilon=8/255`. Follow [instructions here](#pre-trained-models).
* Nov 21, 2019: **Multi-GPU** training support. It is now possible to scale
  CROWN-IBP to the same Large model (trained on 32 TPUs) reported by Gowal et
  al., 2018. The [largest CIFAR model](config/cifar_dm-large_8_255.json) takes
  about 1 day to train on 4x 2080 Ti GPUs.
* Oct 10, 2019: Ordinary **CROWN bounds** have been added for verification
  propose.  See [instructions](#compute-crown-verified-errors). The
  implementation takes advantage of **CNN on GPUs** and is efficient for
  verification.
* July 14, 2019: Code has been further optimized and CROWN-IBP training is
  roughly 2-3 times faster than before.
* Jun 8, 2019: Initial release.

Intro
---------

We aim to train a robust deep neural network that is *verifiable*: given a test
example and a certain perturbation radius `epsilon`, it is able to verify if
there is definitely no adversarial example inside, or there might exist an
adversarial example, through some efficient neural network verification
algorithm. Conversely, PGD based adversarial training is generally not a
verifiable training method, as existing verification methods typically give
vacuous bounds for a PGD-trained network, and there might exists stronger
attacks that can break a PGD-trained model.

The *certified robustness* of a model can be evaluated using *verified error*,
which is a guaranteed *upper bound* of test error under *any* attack.
CROWN-IBP can achieve **6.68\%** verified error on MNIST test set at
`epsilon=0.3`.  The verified error is even lower than the unverified PGD error
(around **12\%**) provided by [Adversarial
training](https://github.com/MadryLab/mnist_challenge) using PGD-based
adversarial training. Previous convex relaxation based method by [Wong et
al.](https://github.com/locuslab/convex_adversarial) has about 30\% to 40\%
verified error.

CROWN-IBP combines [Interval Bound Propagation
(IBP)](https://arxiv.org/abs/1810.12715) training (Gowal et al.  2018) and
[CROWN (Zhang et al. 2018)](http://arxiv.org/pdf/1811.00866.pdf), a tight
linear relaxation based verification method. Ordinary CROWN is very expensive
for training, and it may also over-regularize the network.  IBP alone
outperforms many existing linear relaxation based methods due to its non-linear
representation power (see our paper for more explanation), however since IBP
bounds are very loose at the beginning phase of training, training stability is
a big concern.  We propose to use IBP in a forward pass to compute all
intermediate layers' pre-activation bounds and use a CROWN-style bound to
obtain the final layer's bounds in a backward pass. The combination of IBP and
CROWN gives us an efficient, stable and well-performing certified defense
method, strictly within the framework of robust optimization.

This repository also includes a Pytorch implementation of (ordinary) CROWN,
which computes the full convex relaxation based bounds and can be used to
compute CROWN verified error. Although it is also okay to use the ordinary
CROWN bounds for training (the code supports it, see example config files in
`experimental` folder), but it is very inefficient comparing to CROWN-IBP, and
also produces models with worse verified error due to over-regularization.

Getting Started with the Code
---------

Our program is tested on Pytorch 1.3.0 and Python 3.6/3.7.

We have all training parameters included in JSON files, under the `config` directory.
We provide configuration files which can reproduce all CROWN-IBP models in our paper.

To train CROWN-IBP on MNIST, 10 small models, run:
```bash
python train.py --config config/mnist_crown.json
```

To train CROWN-IBP on MNIST, 8 medium models, run:
```bash
python train.py --config config/mnist_crown_large.json
```

To train CROWN-IBP on MNIST, using the very large model in Gowal et al. to achieve SOTA, run:
```bash
# This uses all GPUs by default.
python train.py --config config/mnist_dm-large_0.4.json
```

To train CROWN-IBP on CIFAR, using the very large model in Gowal et al. to achieve SOTA, run:
```bash
# This uses all GPUs by default.
python train.py --config config/cifar_dm-large_8_255.json
```

You will also find configuration files for 9 small CIFAR models, 8 medium CIFAR models,
10 Fashion-MNIST small models and 8 Fashion-MNIST medium models in `config` folder.
All hyperparameters can be changed in the configuration JSON file.

Pre-trained Models
----------------

CROWN-IBP pretrained models (small and medium sized) used in our paper can be
downloaded
[here](https://download.huan-zhang.com/models/crown-ibp/models_crown-ibp.tar.gz).
The very large model structure (used in Gowal et al., 2018) trained using
CROWN-IBP can be downloaded
[here](https://download.huan-zhang.com/models/crown-ibp/models_crown-ibp_dm-large.tar.gz).

```bash
# Large SOTA models
wget https://download.huan-zhang.com/models/crown-ibp/models_crown-ibp_dm-large.tar.gz
tar xvf models_crown-ibp_dm-large.tar.gz
# Small and medium sized models
wget https://download.huan-zhang.com/models/crown-ibp/models_crown-ibp.tar.gz
tar xvf models_crown-ibp.tar.gz
```

To evaluate the best (and largest) MNIST and CIFAR model (same model structure
as in Gowal et al. 2018, referred to as "dm-large" in our paper), run:

```bash
# Evaluate MNIST with epsilon=0.3
# The default epsilon for MNIST evaluation in config/mnist_dm-large_0.4.json is 0.3.
python eval.py --config config/mnist_dm-large_0.4.json --path_prefix models_crown-ibp_dm-large
# Evaluate MNIST with epsilon=0.4
python eval.py "eval_params:epsilon=0.4" --config config/mnist_dm-large_0.4.json --path_prefix models_crown-ibp_dm-large
# Evaluate CIFAR-10 with epsilon=8/255
python eval.py --config config/cifar_dm-large_8_255.json  --path_prefix models_crown-ibp_dm-large
# Evaluate CIFAR-10 with epsilon=2/255
python eval.py --config config/cifar_dm-large_2_255.json  --path_prefix models_crown-ibp_dm-large
```

Note that the "dm-large"  models have slight different verified errors than the
models reported in our paper (accuracy within +/-0.5%), which were trained
using [Tensorflow](https://github.com/deepmind/interval-bound-propagation.git).
The default `epsilon` for evaluation in `config/mnist_dm-large_0.4.json` is
0.3. The parameter `"eval_params:epsilon=0.4"` overrides the `epsilon` in
configuration file, and the parameter `--path_prefix` changes the default path
that stores models and logs.

The folder `crown-ibp_models` contains several directories, each one
corresponding to a set of (relatively small) models and a `epsilon` value.
They can also be evaluated using the script `eval.py`. For example:

```bash
# Evaluate the 8 medium MNIST models under epsilon=0.4
python eval.py "eval_params:epsilon=0.4" --config config/mnist_crown_large.json --path_prefix crown-ibp_models/mnist_0.4_mnist_crown_large
# Evaluate the 8 medium CIFAR models under epsilon=0.03137 (8/255)
# No epsilon value is given explicitly, so the default in cifar_crown_large.json will be used
python eval.py --config config/cifar_crown_large.json --path_prefix crown-ibp_models/cifar_crown_large_0.03137/
```

The script will report min, median and max verified errors across all models.

Training options
----------------

Training verified models using CROWN-IBP is easy: simply use `train.py` with a
config file, which includes necessary training parameters. For example:

```bash
# Train the largest MNIST model ("dm-large")
python train.py --config config/mnist_dm-large_0.4.json
# Train the largest CIFAR-10 model ("dm-large")
python train.py --config config/cifar_dm-large_8_255.json
```

Multiple models may be defined in a confg file, to train the a specific model,
use the `--model_subset` argument:

```bash
python train.py --config config/mnist_crown_large.json --model_subset 4
```

The argument `--model_subset` selects the 4th model defined in configuration
file. To change perturbation `epsilon`, use the parameter
`"training_params:epsilon=0.4"` which overrides the corresponding `epsilon`
value in configuration file. Other parameters can be overridden in a similar
manner. For `epsilon=0.4` run this command:

```bash
python train.py "training_params:epsilon=0.4" --config config/mnist_crown_large.json --model_subset 4
```

If you want to use multiple GPUs for training, set keyword `"multi_gpu"` under
`"training_params"` section in configuration as true, or equivalently add
`"training_params:multi_gpu=true"` in command line after `train.py`. Then the
program uses all available GPUs in the system. To use less than all GPUs, set
environment variable `CUDA_VISIBLE_DEVICES` before you run.

We also implement baseline methods including IBP (Gowal et al. 2018) and Convex
adversarial polytope (Wong et al. 2018). They can be used by adding command
line parameters to override the training method defined in the JSON file. For
example,

```bash
# for IBP (no kappa terms)
python train.py "training_params:method_params:bound_type=interval" --config config/mnist_crown.json 
# for IBP (with kappa terms as in Gowal et al., 2018)
python train.py "training_params:method=robust_natural" "training_params:method_params:bound_type=interval" --config config/mnist_crown.json
# for Convex adversarial polytope (Wong et al. 2018)
python train.py "training_params:method_params:bound_type=convex-adv" --config config/mnist_crown.json
```

Training Your Own Robust Model Using CROWN-IBP
-----------------

Our implementation can be easily extended to other datasets or models. You only need to do three things:

* Add your dataset loader to `datasets.py` (see examples for MNIST, Fashion-MNIST, CIFAR-10 and SVHN)
* Add your model architecture to `model_defs.py` (you can also reuse any existing models)
* Create a JSON configuration file for training. You can copy from `crown_mnist.json` or `crown_cifar.json`.
You need to change `"dataset"` name in config file, also update model structure in the `"models"` section.

For example, the following in `"models"` section of `"mnist_crown.json"` defines a model with 2 CNN layers:

```json
{
    "model_id": "cnn_2layer_width_1",
    "model_class": "model_cnn_2layer",
    "model_params": {"in_ch": 1, "in_dim": 28, "width": 1, "linear_size": 128}
}
```

where "model\_id" is an unique name for each model, "model\_class" is the
function name to create the model in `model_defs.py`, and "model\_params" is a
dictionary of all parameters passing to the function that creates the model.
Then your will be able to train with CROWN-IBP with your JSON:

```bash
python train.py --config your_config.json
```

Compute CROWN Verified Errors
-------------------

Unlike the [reference
implementation](https://github.com/huanzhang12/RecurJac-and-CROWN) in (Zhang et
al., 2018) which only supports linear layers and is implemented in Numpy, this
repository provides an efficient Pytorch implementation of CROWN for
convolutional neural networks.

In the default setting, the code evaluates IBP verified error. To compute CROWN
verified error, simply set `eval_params:method_params:bound_type=crown-full` in
the evaluation command. Also, you may need to reduce batch size by setting
`eval_params:loader_params:test_batch_size` to a small value, since the CROWN
bounds are memory intensive to compute.

Example:

```
python eval.py "eval_params:epsilon=0.3" "eval_params:loader_params:test_batch_size=128" "eval_params:method_params:bound_type=crown-full" --config config/mnist_crown.json --path_prefix crown-ibp_models/mnist_0.3_mnist_crown --model_subset 1
```

Note that CROWN bounds are relatively loose on (CROWN-)IBP trained models; the
above command produces verified error around 50% (while IBP verified error is
around 10%). However, CROWN should achieve much tighter bounds on naturally
trained, PGD adversarially trained or randomly initialized networks, and is an
useful tool for giving linearized upper and lower bounds for general neural
networks.

Computing CROWN verified error on the entire test set (10,000 images) can take
some time (a few minutes to a few hours).  The time is similar to 1 epoch
training time of (Wong & Kolter, ICML 2018), so it is still feasible to run all
10,000 examples for most models.

Several **bound options** for CROWN are provided:

`same-slope`: when set to `true`, use the same lower bound slope as the upper
bound slope for unstable ReLU neurons. This reduces computation cost by a half,
but usually leads to worse verified error.

`zero-lb`: set the lower bound slope to 0 for unstable ReLU neurons. CROWN
provides a valid bound for any slope from 0 to 1, and these slopes correspond
to dual variables in dual LP formulation; see (Salman et al. 2019) for more
details. Sometimes setting this option to `true` can improve verified error.

`one-lb`: set the lower bound slope to 1 for unstable ReLU neurons. Sometimes
setting this option to `true` can improve verified error.

Example (setting `zero-lb` to `true` to improve CROWN verified error from 53.31%
to 10.61%):

```
python eval.py "eval_params:method_params:bound_opts:zero-lb=true" "eval_params:epsilon=0.3" "eval_params:loader_params:test_batch_size=128" "eval_params:method_params:bound_type=crown-full" --config config/mnist_crown.json --path_prefix crown-ibp_models/mnist_0.3_mnist_crown --model_subset 1
```

Reproducing Paper Results on Different Kappa and Training Schedules
-------------------

In our paper, we evaluate training stability by setting different `epsilon`
schedule length among different models and methods. `epsilon` schedule length
can be controlled through changing the configuration file, or overriding
configuration file parameters as shown below.

To train CROBW-IBP on MNIST, 10 small models with `epsilon=0.3` and schedule length as 10, run this command:
```bash
python train.py training_params:schedule_length=11 --config config/mnist_crown.json 
```
To train IBP on MNIST with no natural CE loss, 10 small models with `epsilon=0.3` and schedule length as 10, run this command:
```bash
python train.py training_params:schedule_length=11 training_params:method_params:bound_type=interval --config config/mnist_crown.json 
```
To train IBP with final `kappa=0.5` on MNIST, 10 small models with `epsilon=0.3` and schedule length as 10, run this command:
```bash
python train.py training_params:schedule_length=11 training_params:method_params:bound_type=interval training_params:method_params:final-kappa=0.5 training_params:method=robust_natural --config config/mnist_crown.json  
```
To train IBP with final `kappa=0` on MNIST, 10 small models with `epsilon=0.3` and schedule length as 10, run this command:
```bash
python train.py training_params:schedule_length=11 training_params:method_params:bound_type=interval training_params:method_params:final-kappa=0 training_params:method=robust_natural --config config/mnist_crown.json
```

References
-------------------

We stand on the shoulders of giants and we greatly appreciate the inspiring
works of pioneers in this field.  Here we list references directly related to
this README. A full bibliography can be found in our paper.

Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin,
Jonathan Uesato, Timothy Mann, and Pushmeet Kohli. "On the effectiveness of
interval bound propagation for training verifiably robust models." arXiv
preprint arXiv:1810.12715 (2018).

Eric Wong, and J. Zico Kolter. Provable defenses against adversarial examples
via the convex outer adversarial polytope. ICML 2018.

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and
Adrian Vladu. "Towards deep learning models resistant to adversarial attacks."
In International Conference on Learning Representations, 2018.

Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel.
Efficient neural network robustness certification with general activation
functions. In Advances in neural information processing systems (NIPS), pp.
4939-4948. 2018.

Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh and Pengchuan Zhang. A Convex
Relaxation Barrier to Tight Robustness Verification of Neural Networks. To
appear in NeurIPS 2019.

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