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Reference implementations for RecurJac, CROWN, FastLin and FastLip (Neural Network verification and robustness certification algorithms) [Do not use this repo, use https://github.com/Verified-Intelligence/auto_LiRPA instead]

Home Page: https://github.com/Verified-Intelligence/auto_LiRPA

License: BSD 2-Clause "Simplified" License

Python 97.83% Shell 2.17%
crown-bounds jacobian-bounds robustness-certification robustness-verification recurjac lipschitz-constant

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recurjac-and-crown's Issues

Certifying robustness on custom network

Hello,

I'm interested in running your certifiable robustness code on a small network (MobileNetV2) that I've trained on CIFAR10. I noticed you have instructions on how to train a network to then certify, but what code should I modify to use my own network?

# python3 main.py --task robustness --numimage 10 --targettype random --norm i --modelfile ../models/mnist_3layer_relu_1024_adv_retrain --layerbndalg crown-adaptive --jacbndalg recurjac --eps 0.2 Loading model ../models/mnist_3layer_relu_1024_adv_retrain 2021-07-26 08:37:45.812888: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually. Model imported using keras Traceback (most recent call last): File "/home/RecurJac-and-CROWN-master/main.py", line 145, in <module> if input_dim[2] == 28 or input_dim[2] == "28": IndexError: list index out of range

base) root@dda65b730206:/home/RecurJac-and-CROWN-master# python3 main.py --task robustness --numimage 10 --targettype random --norm i --modelfile ../models/mnist_3layer_relu_1024_adv_retrain --layerbndalg crown-adaptive --jacbndalg recurjac --eps 0.2
Loading model ../models/mnist_3layer_relu_1024_adv_retrain
2021-07-26 08:37:45.812888: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:tensorflow:No training configuration found in the save file, so the model was not compiled. Compile it manually.
Model imported using keras
Traceback (most recent call last):
File "/home/RecurJac-and-CROWN-master/main.py", line 145, in
if input_dim[2] == 28 or input_dim[2] == "28":
IndexError: list index out of range

Does this repository support robust boundary calculations of CROWN-IBP type?

if layerbndalg == "crown-interval":

I would also like to ask if the “crown-interval" in this code can support CROWN-IBP type of robustness boundary calculation?
And in "boundary_base.py line 188", when determining "layerbndalg == "crown-interval"", is it only for ReLU activation function? Why do I need to call crown_adaptive_bound after interval_bound?is this a CROWN-IBP-like boundary calculation?

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