Adversarial Lab

Sunday, Dec 1, 2019| Tags: Deep Learning, PyTorch, Adversarial ML

Adversarial Lab

This project is a Web-based Tool for visualisation and generation of adversarial examples by attacking ImageNet Models like VGG, AlexNet, ResNet etc.

Visualizing and Comparision of Various Adversarial Attacks on user uploaded images using a simple interface, using the DNN framework Pytorch, using popular SOTA Pretrained TorchVision ModelZoo. The Following Attacks have been implemented so far:

  1. FGSM

    • Fast Gradient Sign Method, Untargeted
    • Fast Gradient Sign Method, Targeted
  2. Iterative

    • Basic Iterative Method, Untargeted
    • Least Likely Class Iterative Method
  3. DeepFool, untargeted

  4. LBFGS, targeted

Coming Soon: Carlini-Wagner l2, and Many More