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An Efficient Adversarial Attack for Tree Ensembles

We study the problem of efficient adversarial attacks on tree based ensembles such as gradient boosting decision trees (GBDTs) and random forests (RFs). Since these models are non-continuous step functions and gradient does not exist, most existing efficient adversarial attacks are not applicable. In our work, we transform the attack problem into a discrete search problem specially designed for tree ensembles, where the goal is to find a valid "leaf tuple" that leads to mis-classification while having the shortest distance to the original input. With this formulation, we show that a simple yet effective greedy algorithm can be applied to iteratively optimize the adversarial example by moving the leaf tuple to its neighborhood within hamming distance 1. More details can be found in our paper:

Chong Zhang, Huan Zhang, Cho-Jui Hsieh, "An Efficient Adversarial Attack for Tree Ensembles", NeurIPS 2020 [poster session]

LT-Attack Setup

Installation on Ubuntu 20.04

Our code requires libboost>=1.66 for thread_pool:

sudo apt install libboost-all-dev

Clone the repo and compile:

git clone git@github.com:chong-z/tree-ensemble-attack.git
cd tree-ensemble-attack
make

Reproduce Results in the Paper

Attack the standard (natural) GBDT model (https://github.com/chenhongge/treeVerification) for the breast_cancer dataset. Construct adversarial examples on L-2 norm perturbation, using 20 threads on 500 test examples:

wget http://download.huan-zhang.com/models/tree-verify/tree_verification_models.tar.bz2
tar jxvf tree_verification_models.tar.bz2

./lt_attack configs/breast_cancer_unrobust_20x500_norm2_lt-attack.json

Attack the standard (natural) RF model for the breast_cancer dataset. Construct adversarial examples on L-2 norm perturbation, using 20 threads on 100 test examples:

./lt_attack configs/breast_cancer_unrobust-rf_20x100_norm2_lt-attack.json

Sample Output

//...
===== Attack result for example 500/500 Norm(2)=0.235702 =====
All Best Norms: Norm(-1)=0.166667 Norm(1)=0.333579 Norm(2)=0.235702.
Average Norms: Norm(-1)=0.235932 Norm(1)=0.369484 Norm(2)=0.282763.
Best Points for example at line 500
1 1:0.07214075340 2:0.11111100000 4:0.16666650810 6:0.11111100000 7:0.16666650810
Results for config:configs/breast_cancer_unrobust_20x500_norm2_lt-attack.json
Average Norms: Norm(-1)=0.235932 Norm(1)=0.369484 Norm(2)=0.282763
--- Timing Metrics ---
|collect_histogram| disabled
## Actual Examples Tested:496
## Time per point: 0.00141016

Configuration File Parameters

We provide sample config files in config/ which use the following parameters:

search_mode: The attack method to use. Choose from 'lt-attack' (ours), 'naive-leaf', 'naive-feature'.
norm_type: The objective norm order. Supports 1, 2, and -1 (for L-Inf).
num_point: Number of test examples to attack. We use 500 test examples in most of our experiments.
num_threads: CPU threads per task. We use 20 physical threads per task in most of our experiments.
num_attack_per_point: Number of initial adversarial examples. Usually set to the same as num_threads.
enable_early_return: Use early return to speed up the search in Neighbor_1(C'). Usually set to true.

Additional dataset related parameters:

model: Path to the JSON file dumped from XGBoost models using bst.dump_model('bar.json', dump_format='json'). See https://xgboost.readthedocs.io/en/latest/python/python_intro.html#training
inputs: Path to the test example file in LIBSVM format.
num_classes: Number of classes in the dataset.
num_features: Number of features in the dataset.
feature_start: The index of the first feature, could be 0 or 1 on different datasets.

Run Baselines

SignOPT, HSJA, and Cube

pip3 install xgboost==1.0.2 sklearn
# Choose |'search_mode'| from 'signopt', 'hsja', and 'cube'. We provide a few sample configs:
python3 baselines/test_attack_cpu.py --config_path=configs/breast_cancer_unrobust_20x500_norm2_cube.json

MILP

# Use |'search_mode': 'milp'|. Requires the Gurobi Solver installed.
python3 baselines/xgbKantchelianAttack.py --config_path=configs/breast_cancer_unrobust_20x500_norm2_milp.json

RBA-Appr

# RBA-Appr requires training data which can be downloaded from https://github.com/chenhongge/RobustTrees.
# [ICML 2019] Hongge Chen, Huan Zhang, Duane Boning, and Cho-Jui Hsieh, Robust Decision Trees Against Adversarial Examples
# The author published their datasets in the URL below.
mkdir raw_data
cd raw_data
wget https://raw.githubusercontent.com/chenhongge/RobustTrees/master/data/download_data.sh
sh download_data.sh
cd ..

# Use |'search_mode': 'region'|, and add |"train_data": "raw_data/TRAIN_DATA_NAME"| to the corresponding config file.
# We provide a sample config:
./lt_attack configs/breast_cancer_unrobust_20x500_norm2_region.json

Known Issues

The JSON dump of XGBoost models offer precision up to 8 digits, however the difference between certain feature split threholds may be smaller than 1e-8 in the original XGBoost model. For this reason the model created from the JSON dump may produce a different prediction on certain examples than the original XGBoost model, and we manually verify that each produced adversarial example is valid under the JSON dump.

Bibtex

@inproceedings{zhang2020efficient,
 author = {Zhang, Chong and Zhang, Huan and Hsieh, Cho-Jui},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {16165--16176},
 publisher = {Curran Associates, Inc.},
 title = {An Efficient Adversarial Attack for Tree Ensembles},
 url = {https://proceedings.neurips.cc/paper/2020/file/ba3e9b6a519cfddc560b5d53210df1bd-Paper.pdf},
 volume = {33},
 year = {2020}
}

Credits

nlohmann/json*: https://github.com/nlohmann/json.
.clang-format: https://cs.chromium.org/chromium/src/.clang-format.
See paper for the full list of references.