DeepCover explains image classifiers using statistical fault localization and causal theory.
docker run -v ${PWD}:/home -it ubuntu:20.04
apt-get update && apt-get install pip git ffmpeg libsm6 libxext6 && pip install matplotlib seaborn tensorflow==2.3.0 keras==2.4.3 numpy==1.18.0 scipy==1.4.1 opencv-python && cd home && git clone https://github.com/theyoucheng/deepcover
python ./src/deepcover.py --help
usage: deepcover.py [-h] [--model MODEL] [--inputs DIR] [--outputs DIR]
[--measures [...]] [--measure MEASURE] [--mnist-dataset]
[--normalized-input] [--cifar10-dataset] [--grayscale]
[--vgg16-model] [--inception-v3-model] [--xception-model]
[--mobilenet-model] [--attack] [--text-only]
[--input-rows INT] [--input-cols INT]
[--input-channels INT] [--x-verbosity INT]
[--top-classes INT] [--adversarial-ub FLOAT]
[--adversarial-lb FLOAT] [--masking-value INT]
[--testgen-factor FLOAT] [--testgen-size INT]
[--testgen-iterations INT] [--causal] [--wsol FILE]
[--occlusion FILE]
python ./src/deepcover.py --mobilenet-model --inputs data/panda --outputs outs --testgen-size 200
--mobilenet-model pre-trained keras model
--inputs input images folder
--outputs output images folder
--testgen-size the number of input mutants to generate for explaining (by default, it is 2,000)
python src/deepcover.py --mobilenet-model --inputs data/panda/ --outputs outs --measures tarantula zoltar --x-verbosity 1 --masking-value 0
--measures to specify the SFL measures for explaining: tarantula, zoltar, ochiai, wong-ii
--x-verbosity to control the verbosity level of the explanation results
--masking-value to control the masking color for mutating the input image
python ./sfl-src/sfl.py --mobilenet-model --inputs data/panda --outputs outs --causal --testgen-iterations 50
--causal to trigger the causal explanation
--testgen-iterations number of individual causal refinement calls; by default, it’s 1
python src/deepcover.py --model models/gtsrb_backdoor.h5 --input-rows 32 --input-cols 32 --inputs data/gtsrb/ --outputs outs
--input-rows row number for the input image
--input-cols column number for the input image
@inproceedings{sck2021,
AUTHOR = { Sun, Youcheng
and Chockler, Hana
and Kroening, Daniel },
TITLE = { Explanations for Occluded Images },
BOOKTITLE = { International Conference on Computer Vision (ICCV) },
PUBLISHER = { IEEE },
PAGES = { 1234--1243 },
YEAR = { 2021 }
}
@inproceedings{schk2020,
AUTHOR = { Sun, Youcheng
and Chockler, Hana
and Huang, Xiaowei
and Kroening, Daniel},
TITLE = {Explaining Image Classifiers using Statistical Fault Localization},
BOOKTITLE = {European Conference on Computer Vision (ECCV)},
YEAR = { 2020 }
}