CS776A: Deep Learning for Computer Vision

Course Project: Adversarial Training Is All You Need

Table of Contents

1. Code
2. Implementation References
3. Presentations
4. Team Details

Code

• Abstract is present as CS776_Project_Abstract_grp1.pdf
• Final Report is present as CS776_Project_Report_grp1.pdf
• Presentations are present in presentations/ directory.
• Colab notebooks are present in offline form in noteboooks/ directory. You may upload them on colab to run, or simply install all the dependencies to run locally.
• Trained weights are present in weights/ directory.
• The attacks and trainers have been implemented and are present in src/ directory.
• Sample tests showing how to import and run code from src/ are present in test/ directory.
• To install the dependencies present in requirements.txt, run the following code

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Implementation References

1. Projected Gradient Descent : https://arxiv.org/abs/1706.06083
2. Fast Gradient : https://arxiv.org/abs/1412.6572
3. Poisoning Backdoor Attack : https://arxiv.org/abs/1708.06733
4. Clean Label Backdoor Attack : https://people.csail.mit.edu/madry/lab/cleanlabel.pdf
5. Adversarial Trainer: https://arxiv.org/abs/1705.07204

Presentations

• Introduction
• MidTerm
• EndTerm

Team Details

• Name : Four of a Kind

• Members :

Name	Roll No.
Antreev Singh Brar	190163
Anubhav Kalyani	190164
Gurbaaz Singh Nandra	190349
Pramodh V Gopalan	190933