update

README.md

@@ -1,49 +1,71 @@
-# DeepCover
+# DeepCover: Uncover the Truth Behind AI
 ![Uncover the truth behind AI](images/deepcover.gif) 
 
-DeepCover is a tool for [testing](https://dl.acm.org/doi/abs/10.1145/3358233) and 
-[debugging](https://arxiv.org/abs/1908.02374) Deep Neural Network (DNNs).
+![alt text](images/deepcover-logo)
 
-## To start running the Statistical Fault Localization (SFL) based explaining:
+DeepCover explains image classifiers using [statistical fault lolization](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730392.pdf) and 
+[causal theory](https://openaccess.thecvf.com/content/ICCV2021/papers/Chockler_Explanations_for_Occluded_Images_ICCV_2021_paper.pdf).
+
+# Install and Setup
+#### Pre-Installed: `Conda 4.7.11`
+#### All commands were tested on macOS 10.14.6 and Ubuntu 20.04
+```
+conda create --name deepcover-env python==3.7
+conda activate deepcover-env
+conda install opencv matplotlib seaborn
+pip install tensorflow==2.3.0  keras==2.4.3
+```
+
+# Hello DeepCover
 ```
-python ./sfl-src/sfl.py --mobilenet-model --inputs data/ --outputs outs
+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]
 ```
 
-# More options
+
+## To start running the Statistical Fault Localization (SFL) based explaining:
 ```
-python ./sfl-src/sfl.py --help
-usage: sfl.py [-h] [--model MODEL] [--inputs DIR] [--outputs DIR]
-              [--measures zoltar, tarantula ... [zoltar, tarantula ... ...]]
-              [--measure zoltar, tarantula ...] [--mnist-dataset]
-              [--cifar10-dataset] [--grayscale] [--vgg16-model]
-              [--inception-v3-model] [--xception-model] [--mobilenet-model]
-              [--attack] [--text-only] [--input-rows INT] [--input-cols INT]
-              [--input-channels INT] [--top-classes INT]
-              [--adversarial-ub FLOAT] [--adversarial-lb FLOAT]
-              [--adversarial-value FLOAT] [--testgen-factor FLOAT]
-              [--testgen-size INT] [--testgen-iterations INT]
+python ./sfl-src/sfl.py --mobilenet-model --inputs data/panda --outputs outs
 ```
 
-# Dependencies
-We suggest create an environment using `conda`, `tensorflow>=2.0.0`
+## To start running the causal theory based explaining:
 ```
-conda create --name deepcover
-source activate deepcover
-conda install keras
-conda install opencv
-conda install pillow
+python ./sfl-src/sfl.py --mobilenet-model --inputs data/panda --outputs outs --causal --testgen-iterations 50
 ```
 
 # Publications
 ```
+@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 },
+YEAR = { 2020 }
 }
 ```