Adversarial Robustness Transfer Learning: Research on Defense Strategies for Image Classification Neural Networks

This project focuses on adversarial robustness transfer learning, exploring how to transfer knowledge from pre-trained robust models to new tasks to reduce the high computational cost of adversarial training. The project provides complete experimental code, laying an empirical foundation for building safer AI systems, covering key technologies such as adversarial training, transfer learning, PGD attacks, and formal verification.

Deep learning models perform excellently in image classification tasks but are vulnerable to adversarial examples—tiny perturbations can lead to incorrect predictions, posing significant risks in safety-critical domains. Adversarial training is an effective defense method, but its computational cost is extremely high. Core question: Can the robustness of pre-trained robust models be transferred to new tasks to avoid adversarial training from scratch?

The project codebase is divided into three parts: 1. Adversarial training toolbox (scripts for standard/adversarial training and transfer learning variants); 2. Formal verification scripts (based on the VERONA framework, providing theoretical guarantees of robustness); 3. Plotting scripts (result visualization). Experimental workflow: Adversarial training on source task → Transfer to target task (strategies like direct fine-tuning/transfer + adversarial training) → Robustness evaluation (VERONA verification + PGD attack testing) → Result analysis.

Technical choices: PGD as the main attack/training method (generating strong adversarial examples); VERONA for formal verification (mathematically guaranteeing robustness); datasets covering CIFAR and EMNIST to enhance generalization. Research findings: Robustness can be transferred; transfer efficiency is improved compared to training from scratch; different transfer strategies have varying effects; formal verification has practical value.

Industrial deployment: Pre-trained robust models in the cloud, transferred to edge devices to reduce costs; Academic research: Provides a complete experimental framework to facilitate subsequent expansion; Safe AI ecosystem: Promotes the popularization of robust AI in safety-critical fields such as autonomous driving and medical diagnosis.

Current limitations: Experiments are based on standard datasets, and the effect in real scenarios needs to be verified; high verification cost limits model scale; transfer effect is affected by task similarity. Future directions: Explore large-scale model transfer; study the impact of unsupervised/self-supervised pre-training; develop efficient verification algorithms; expand to complex tasks such as object detection.