pytorch-adversarial-attackers 0.1

A PyTorch library for evaluating model robustness against FGSM, PGD, and CW attacks.

PyTorch Adversarial Attackers

A PyTorch-based library for evaluating model robustness against adversarial attacks: FGSM, PGD, and CW attacks. Designed to work with all PyTorch models like ResNet, VGG16, MobileNet, etc.

Installation

pip install pytorch-adversarial-attackers

Usage Example

import torch
import torchvision.models as models
from adversarial_eval.attacks import FGSM, PGD, CW
from adversarial_eval.evaluate import evaluate

# Load pre-trained model
model = models.resnet18(pretrained=True).eval()

# Load image
image = torch.rand((1, 3, 224, 224))  # Example input
label = torch.tensor([0])  # Example label

# Apply attacks
fgsm_attack = FGSM(model, epsilon=0.03)
adv_image = fgsm_attack(image, label)

# Evaluate robustness
evaluate(model, image, label)

Attacks Implemented

• FGSM (Fast Gradient Sign Method)
• PGD (Projected Gradient Descent)
• CW (Carlini & Wagner Attack)
• GITHUB LINK https://github.com/santhosh1705kumar/pytorch-adversarial-attackers