Adversarial Robustness Research

Evaluating Model Robustness to Adversarial Attacks on CIFAR-10

Evaluating Model Robustness to Adversarial Attacks

This research project investigates the vulnerability of deep learning models to adversarial attacks and explores methods to improve their robustness. The study focuses on ResNet-50 models trained on the CIFAR-10 dataset, examining their performance under various adversarial attack scenarios.

Key Findings

Our research revealed critical insights about model vulnerability:

  • Baseline Performance: ResNet-50 models achieve 87% accuracy on clean CIFAR-10 test data
  • Attack Vulnerability: Under Fast Gradient Sign Method (FGSM) attacks, accuracy drops dramatically to just 9%
  • Robustness Recovery: Through adversarial training, we successfully recovered 55% of the model’s robustness

Technical Approach

The project implemented several key components:

  1. Adversarial Attack Generation: Implemented FGSM and other attack methods using PyTorch
  2. Model Training: Trained ResNet-50 models with both standard and adversarial training approaches
  3. Evaluation Framework: Developed comprehensive metrics to assess model robustness
  4. Defense Strategies: Explored various defense mechanisms including adversarial training

Significance for AI Safety

This work is particularly relevant for deploying AI systems in safety-critical applications where adversarial robustness is essential. The findings contribute to the broader field of AI safety and trustworthy machine learning.

Technologies Used

  • PyTorch for deep learning implementation
  • NumPy for numerical computations
  • CIFAR-10 dataset for evaluation
  • ResNet-50 architecture as the base model
Visualization of adversarial attacks, robustness metrics, and training progress throughout the research project.
Download the complete research paper for detailed methodology, results, and analysis.

Future Work

This research opens several promising directions:

  • Exploring more sophisticated attack methods
  • Investigating transfer learning for robust models
  • Developing theoretical frameworks for understanding adversarial vulnerability
  • Applying findings to larger-scale models and datasets

The project demonstrates the importance of considering adversarial robustness in machine learning system design and contributes valuable insights to the AI safety community.