WHAT v0.2.1

v0.2.1

WHAT v0.1.0

WHite-box Adversarial Toolbox (WHAT)

A Python Library for Deep Learning Security that focuses on Real-time White-box Attacks.

Installation

pip install whitebox-adversarial-toolbox

Usage (CLI)

Usage: what [OPTIONS] COMMAND [ARGS]...

  The CLI tool for WHitebox-box Adversarial Toolbox (what).

Options:
  --help  Show this message and exit.

Commands:
  attack   Manage Attacks
  example  Manage Examples
  model    Manage Deep Learning Models

Useful commands:

# List supported models
$ what model list

# List supported Attacks
$ what attack list

# List available examples
$ what example list

Supported Models

[x] 1 : YOLOv3      (    Darknet    )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
[x] 2 : YOLOv3      (   Mobilenet   )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
[x] 3 : YOLOv3 Tiny (    Darknet    )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
[x] 4 : YOLOv3 Tiny (   MobileNet   )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
[x] 5 : YOLOv4      (    Darknet    )   Object Detection        YOLOv4 pretrained on MS COCO dataset.
[x] 6 : YOLOv4 Tiny (    Darknet    )   Object Detection        YOLOv4 Tiny pretrained on MS COCO dataset.
[x] 7 : SSD         ( MobileNet  v1 )   Object Detection        SSD pretrained on VOC-2012 dataset.
[x] 8 : SSD         ( MobileNet  v2 )   Object Detection        SSD pretrained on VOC-2012 dataset.
[x] 9 : FasterRCNN  (     VGG16     )   Object Detection        Faster-RCNN pretrained on VOC-2012 dataset.

Supported Attacks

Use what attack list to list available attacks:

1 : TOG Attack  Object Detection
2 : PCB Attack  Object Detection

• A Man-in-the-Middle Attack against Object Detection Systems.
• Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems.

Available Examples

           Demo                Type             Description
--------------------------------------------------------------------------------
1 :     Yolov3 Demo      Model Inference        Yolov3 Object Detection.
2 :     Yolov4 Demo      Model Inference        Yolov4 Object Detection.
3 :   FasterRCNN Demo    Model Inference        FRCNN Object Detection.
4 : MobileNet SSD Demo   Model Inference        MobileNet SSD Object Detection.
5 :  TOG Attack Demo    Adversarial Attack      Real-time TOG Attack against Yolov3 Tiny.
6 :  PCB Attack Demo    Adversarial Attack      Real-time PCB Attack against Yolov3 Tiny.