Merge remote-tracking branch 'origin/main' into dev_1.8.0

Author: Beat Buesser

README-cn.md

@@ -17,6 +17,7 @@
 [![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
 [![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox)](https://pepy.tech/project/adversarial-robustness-toolbox)
 [![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox/month)](https://pepy.tech/project/adversarial-robustness-toolbox)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5090/badge)](https://bestpractices.coreinfrastructure.org/projects/5090)
 
 
 对抗性鲁棒性工具集（ART）是用于机器学习安全性的Python库。ART提供的工具可

README.md

@@ -17,6 +17,7 @@
 [![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
 [![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox)](https://pepy.tech/project/adversarial-robustness-toolbox)
 [![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox/month)](https://pepy.tech/project/adversarial-robustness-toolbox)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5090/badge)](https://bestpractices.coreinfrastructure.org/projects/5090)
 
 [中文README请按此处](README-cn.md)
 

notebooks/README.md

@@ -54,9 +54,10 @@ demonstrates ART with TensorFlow v2 using tensorflow.keras without eager executi
 or [attack_feature_adversaries_tensorflow_v2.ipynb](attack_feature_adversaries_tensorflow_v2.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_feature_adversaries_tensorflow_v2.ipynb)]
 show how to use ART to create feature adversaries ([Sabour et al., 2016](https://arxiv.org/abs/1511.05122)).
 
-[attack_adversarial_patch.ipynb](adversarial_patch/attack_adversarial_patch.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_adversarial_patch.ipynb)]
+[attack_adversarial_patch.ipynb](adversarial_patch/attack_adversarial_patch.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_patch/attack_adversarial_patch.ipynb)]
 shows how to use ART to create real-world adversarial patches that fool real-world object detection and classification 
 models.
+[attack_adversarial_patch_TensorFlowV2.ipynb](adversarial_patch/attack_adversarial_patch.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_patch/attack_adversarial_patch_TensorFlowV2.ipynb)]  TensorFlow v2 specific attack implementation.
 
 <p align="center">
   <img src="../utils/data/images/adversarial_patch.png?raw=true" width="200" title="adversarial_patch">

requirements.txt

@@ -1,17 +1,17 @@
 # base
 numpy>=1.18.0
-scipy==1.7.0
-matplotlib==3.4.2
+scipy==1.7.1
+matplotlib==3.4.3
 scikit-learn>=0.22.2,<0.24.3
 six==1.16.0
 Pillow==8.3.1
-tqdm==4.61.2
+tqdm==4.62.2
 statsmodels==0.12.2
 pydub==0.25.1
 resampy==0.2.2
 ffmpeg-python==0.2.0
 cma==3.1.0
-pandas==1.3.0
+pandas==1.3.2
 librosa==0.8.1
 numba~=0.53.1
 opencv-python
@@ -28,11 +28,11 @@ mxnet-native==1.8.0.post0
 torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
 torchvision==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
-catboost==0.26
+catboost==0.26.1
 GPy==1.10.0
 lightgbm==3.2.1
 xgboost==1.4.2
-kornia~=0.5.6
+kornia~=0.5.8
 tensorboardX==2.4
 lief==0.11.5
 
@@ -47,7 +47,7 @@ pytest~=6.2.4
 pytest-flake8~=1.0.7
 pytest-mock~=3.6.1
 pytest-cov~=2.12.1
-codecov~=2.1.11
+codecov~=2.1.12
 requests~=2.26.0