what

  1r'''
  2WHite-box Adversarial Toolbox (WHAT) is a python library for Deep Learning Security that focuses on realtime white-box attacks.
  3
  4## Installation
  5
  6```
  7pip install whitebox-adversarial-toolbox
  8```
  9
 10Then you can use the cli tool `what` to try real-time adversarial attacks.
 11
 12```
 13sage: what [OPTIONS] COMMAND [ARGS]...
 14
 15  The CLI tool for WHite-box Adversarial Toolbox (WHAT).
 16
 17Options:
 18  --help  Show this message and exit.
 19
 20Commands:
 21  attack   Manage Attacks
 22  example  Manage Examples
 23  model    Manage Deep Learning Models
 24```
 25
 26<br />
 27
 28## what.models
 29
 30Use `what model list` to list available models:
 31
 32```
 33                Model                      Model Type           Description
 34----------------------------------------------------------------------------------------------------
 35[ ] 1 : YOLOv3      (    Darknet    )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
 36[ ] 2 : YOLOv3      (   Mobilenet   )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
 37[ ] 3 : YOLOv3 Tiny (    Darknet    )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
 38[ ] 4 : YOLOv3 Tiny (   MobileNet   )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
 39[ ] 5 : YOLOv4      (    Darknet    )   Object Detection        YOLOv4 pretrained on MS COCO dataset.
 40[ ] 6 : YOLOv4 Tiny (    Darknet    )   Object Detection        YOLOv4 Tiny pretrained on MS COCO dataset.
 41[ ] 7 : SSD         ( MobileNet  v1 )   Object Detection        SSD pretrained on VOC-2012 dataset.
 42[ ] 8 : SSD         ( MobileNet  v2 )   Object Detection        SSD pretrained on VOC-2012 dataset.
 43[ ] 9 : FasterRCNN  (     VGG16     )   Object Detection        Faster-RCNN pretrained on VOC-2012 dataset.
 44```
 45
 46Use `what model download` to download pre-trained models:
 47
 48```
 49                Model                      Model Type           Description
 50----------------------------------------------------------------------------------------------------
 51[x] 1 : YOLOv3      (    Darknet    )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
 52[x] 2 : YOLOv3      (   Mobilenet   )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
 53[x] 3 : YOLOv3 Tiny (    Darknet    )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
 54[x] 4 : YOLOv3 Tiny (   MobileNet   )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
 55[x] 5 : YOLOv4      (    Darknet    )   Object Detection        YOLOv4 pretrained on MS COCO dataset.
 56[x] 6 : YOLOv4 Tiny (    Darknet    )   Object Detection        YOLOv4 Tiny pretrained on MS COCO dataset.
 57[x] 7 : SSD         ( MobileNet  v1 )   Object Detection        SSD pretrained on VOC-2012 dataset.
 58[x] 8 : SSD         ( MobileNet  v2 )   Object Detection        SSD pretrained on VOC-2012 dataset.
 59[x] 9 : FasterRCNN  (     VGG16     )   Object Detection        Faster-RCNN pretrained on VOC-2012 dataset.
 60
 61Please input the model index: 
 62```
 63
 64<br />
 65
 66## what.attacks
 67
 68Use `what attack list` to list available attacks:
 69
 70```
 711 : TOG Attack  Object Detection
 722 : PCB Attack  Object Detection
 73```
 74
 75Related Papers:
 76
 77- [Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems](https://ieeexplore.ieee.org/document/9325397).
 78- [A Man-in-the-Middle Attack against Object Detection Systems](https://arxiv.org/abs/2208.07174).
 79
 80<br />
 81
 82## what.examples
 83
 84Use `what example list` to list available examples:
 85
 86```
 87           Demo                Type             Description
 88--------------------------------------------------------------------------------
 891 :     Yolov3 Demo      Model Inference        Yolov3 Object Detection.
 902 :     Yolov4 Demo      Model Inference        Yolov4 Object Detection.
 913 :   FasterRCNN Demo    Model Inference        FRCNN Object Detection.
 924 : MobileNet SSD Demo   Model Inference        MobileNet SSD Object Detection.
 935 :  TOG Attack Demo    Adversarial Attack      Real-time TOG Attack against Yolov3 Tiny.
 946 :  PCB Attack Demo    Adversarial Attack      Real-time PCB Attack against Yolov3 Tiny.
 95```
 96
 97Use `what example run` to run examples.
 98
 99```
100           Demo                Type             Description
101--------------------------------------------------------------------------------
1021 :     Yolov3 Demo      Model Inference        Yolov3 Object Detection.
1032 :     Yolov4 Demo      Model Inference        Yolov4 Object Detection.
1043 :   FasterRCNN Demo    Model Inference        FRCNN Object Detection.
1054 : MobileNet SSD Demo   Model Inference        MobileNet SSD Object Detection.
1065 :  TOG Attack Demo    Adversarial Attack      Real-time TOG Attack against Yolov3 Tiny.
1076 :  PCB Attack Demo    Adversarial Attack      Real-time PCB Attack against Yolov3 Tiny.
108
109Please input the example index: 
110```
111
112<br />
113
114## what.utils
115
116This module implements several utility functions.
117
118<br />
119
120'''
121
122# Project Imports
123from what import models
124from what import attacks
125from what import utils
126
127# Semantic Version
128__version__ = "0.2.2"