Adversarial Attack Methods Guide

Adversarial attacks are a critical topic in the field of machine learning and AI. This guide will provide an overview of various methods used in adversarial attacks.

Types of Adversarial Attacks

Evasion Attacks: These attacks aim to fool the model into making incorrect predictions.
Poisoning Attacks: In these attacks, malicious data is injected into the training dataset to corrupt the model.
Inference Attacks: These attacks attempt to extract sensitive information from a model.

Common Methods

Gradient-Based Attacks: These attacks use the model's gradient to generate adversarial examples.
FoolBox: A popular tool for generating adversarial examples.
C&W Attack: A robust method for generating adversarial examples.

Example of an Adversarial Example

Here's an example of an adversarial example generated using the C&W attack:

import torch
import torchvision.transforms as transforms
from torchvision.models import ResNet18

# Load the model
model = ResNet18(pretrained=True)
model.eval()

# Load the image
image = Image.open("cat.jpg")
transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()])
image = transform(image).unsqueeze(0)

# Generate the adversarial example
adversarial_example = generate_adversarial_example(model, image)

# Show the original and adversarial images
plt.imshow(image.squeeze())
plt.title("Original Image")
plt.show()

plt.imshow(adversarial_example.squeeze())
plt.title("Adversarial Example")
plt.show()

For more information on generating adversarial examples, check out our Adversarial Example Generation Guide.

Resources