This is repository for paper ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation.
This repo also include submission file for TrojAI compeition different rounds. Currently, it includes the TrojAI competition round 1 submission. Round 1 code is the submission for 20200725T153001 of Perspecta team. This submission has 0.30 cross entropy and 0.91 roc-auc on test set and 0.281 cross entropy loss and 0.92 roc-auc on holdout set.
Python 2, tensoflow=1.12.0, keras=2.2.4, imageio, numpy, pickle, h5py
Here we provide binary file to execute ABS in tensorflow and keras.
You can edit config.json to change different models and settings for ABS. models contain 20 benign models and 21 compromised models. You can edit config.json to choose different models.
The seed images for CIFAR-10 dataset is in cifar_seed_10.pkl which contains 10 seed images, ABS reads in this file and perform analysis on these data. cifar_seed_50.pkl contains 50 seed images and running ABS on more images can increase stability.
The preprossing code of input images is written in preprocess.py. ABS calls cifar.py and to provide your own preprocess function, just change the code in cifar.py.
To run the code,
python run_abs.py
The program will output highest REASR for the model provided in config.json.
Triggers with over 80% REASR is shown imgs folder. deltas and masks store the numpy array for such triggers.
Currently, this version of ABS only work on CIFAR-10 dataset and may not support some structure. Support for more dataset and structure is coming soon.
Currently, ABS assumes the activation layer and conv/dense layer are seperated, i.e. the conv/dense layers do not have activation function and there is an activation layer after each dense/conv layer.
Please refer to reformat_model.py to see how to seperate activation layers from conv/dense layer.
Yingqi Liu, liu1751@purdue.edu