Jailbreaking Deep Models

A comprehensive implementation of adversarial attacks on ImageNet classifiers, demonstrating the vulnerability of deep neural networks to carefully crafted perturbations.

Overview

This project implements and evaluates three types of adversarial attacks on state-of-the-art image classifiers:

• Fast Gradient Sign Method (FGSM)
• Projected Gradient Descent (PGD)
• Localized Patch Attacks

The attacks are tested on ResNet-34 and DenseNet-121 models trained on ImageNet-1K, showing how even small perturbations can significantly degrade model performance.

Features

• Implementation of FGSM, PGD, and patch-based adversarial attacks
• Support for both ResNet-34 and DenseNet-121 architectures
• Comprehensive evaluation metrics including top-1 and top-5 accuracy
• Visualization tools for comparing original and adversarial examples

Installation

# Clone the repository
git clone https://github.com/ftaghiyev/Jailbreaking-Deep-Models.git
cd Jailbreaking-Deep-Models

# Install dependencies
pip install -r requirements.txt

Results

Our experiments demonstrate significant degradation in model performance across all attack methods. For detailed results and analysis, please refer to the full report.