SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications (AAAI 2020)

Paper | video | supplementary material

Tensorflow implementation of the paper in AAAI 2020. The paper tries to address the robustness of Deep Neeural Networks, but not from pixel-level perturbation lense, rather from semantic lense in which the perturbation happens in the latent parameters that generate the image. This type of robustness is important for safety-critical applications like self-driving cars in which tolerance of error is very low and risk of failure is high.

SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
Abdullah Hamdi, Matthias Muller, Bernard Ghanem

Citation

If you find this useful for your research, please use the following.

@inproceedings{hamdi2020sada,
title = {{SADA:} Semantic Adversarial Diagnostic Attacks for Autonomous Applications},
author = {Abdullah Hamdi and Matthias Muller and Bernard Ghanem},
booktitle = {AAAI Conference on Artificial Intelligence},
year = 2020
}

Prerequisites

• Linux
• Python 2 or 3
• NVIDIA GPU (11G memory or larger) + CUDA cuDNN
• Blender 2.79

Getting Started

Installation

• install Blender with the version blender-2.79b-linux-glibc219-x86_64 and add it to your PATH by adding the command export PATH="${PATH}:/home/PATH/TO/blender-2.79b-linux-glibc219-x86_64" in /home/.bashrc file . make sure at the end that you can run blender command from your shell script.

• Clone this repo:

git clone https://github.com/ajhamdi/SADA
cd SADA

• install the following conda environment as follows:

conda env create -f environment.yaml
conda activate sada

• Download the dataset that contains the 3D shapes and the environments from this link and place the folder in the same project dir with name 3d/training_pascal.

• Download the weights for YOLOv3 from this link and place in the detectos dir.

Dataset

• We collect 100 3D shapes from 10 classes from ShapeNet and Pascal3D . All the sahpes are available inside the blender environment 3d/training_pascal/training.blend file. The classes are the following

1. aeroplane
2. bench
3. bicycle
4. boat
5. bottle
6. bus
7. car
8. chair
9. dining table
10. motorbike
11. train
12. truck

• The parameters that control the environment are 8 as follows

1. camera distance to the object
2. camera azimuth angle
3. camera pitch angle
4. light source azimuth angle
5. light source pitch angle
6. color of the object (R-channel)
7. color of the object (G-channel)
8. color of the object (B-channel)

Generating images from the 3D environment for a specific class with random parameters and storing the 2D dataset in the folder generated

python main.py --is_gendist=True --class_nb= 0 --dataset_nb= 0 --gendist_size= 10000

• is_gendist : is the option to generate distribution of parameters and images
  
• class_nb the class of the 12 classes above to generate
  
• dataset_nb : is the number assigned to the dataset generated
  
• gendist_size : the number of inages generated
  
  
  

training BBGAN

python main.py --is_train=True --valid_size=50 --log_frq=10 --batch_size=32 --induced_size=50 --nb_steps=600 --learning_rate_t=0.0001 --learning_rate_g=0.0001

• class_nb the class of the 12 classes above to generate
  
• dataset_nb : is the number assigned to the dataset generated
  
• nb_steps : is the number of training steps of the GAN
  
• log_frq=10 : how often u save the weights of the network
  
• induced_size: is the number of best samples that will be picked out of the total numberof generated images
  
• learning_rate_g: the learning rate forf the generator
• learning_rate_t: the learning rate forf the discrminator
• valid_size : is the number of paramters u will be generating eventually for evaluation of the BBGAN
  

Self-Driving with CARLA

• coming soon

UAV racing with Sim4CV

• coming soon