DD-RobustBench: Evaluating Adversarial Robustness in Dataset Distillation

Centrala begrepp

Dataset distillation methods exhibit improved robustness, with potential for enhancing model training.

Sammanfattning

In this work, a benchmark is introduced to evaluate the adversarial robustness of distilled datasets. The study covers various dataset distillation methods, adversarial attack techniques, and large-scale datasets. Results show that distilled datasets generally display better robustness than original datasets, with robustness decreasing as the number of images per class (IPC) increases. Incorporating distilled images into training batches enhances model robustness, acting as a form of adversarial training. The paper provides new insights into evaluating dataset distillation and suggests future research directions.

Directory:

• Introduction to Dataset Distillation
  • Dataset distillation compresses datasets while maintaining performance.
• Importance of Adversarial Robustness Evaluation
  • Existing works focus on accuracy but overlook robustness.
• Proposed Benchmark for Adversarial Robustness Evaluation
  • Extensive evaluations using state-of-the-art methods and attacks.
• Frequency Domain Analysis of Distilled Data
  • Investigating frequency characteristics to understand knowledge extraction.
• Enhancing Model Robustness with Distilled Data
  • Incorporating distilled images improves model robustness.

Statistik

"Our investigation of the results indicates that distilled datasets exhibit better robustness than the original datasets in most cases."
"Models trained using distilled CIFAR-10, CIFAR-100, and TinyImageNet datasets demonstrate superior robustness compared to those trained on the original dataset."

Citat

Viktiga insikter från

DD-RobustBench

by Yifan Wu,Jia... på arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13322.pdf

Djupare frågor

How can frequency domain analysis enhance our understanding of dataset distillation?

Frequency domain analysis can provide valuable insights into the characteristics of distilled datasets. By examining the energy distribution between low-frequency components (LFC) and high-frequency components (HFC) in images, we can uncover patterns that may influence model performance. For example, a higher proportion of HFC in distilled images could indicate the presence of intricate details that might impact robustness against adversarial attacks. Additionally, comparing the frequency properties of original and distilled datasets through techniques like principal component analysis can reveal similarities or differences that shed light on how knowledge is condensed during distillation.

What are the implications of balancing accuracy and robustness in dataset distillation?

Balancing accuracy and robustness is crucial in dataset distillation to ensure that compressed datasets not only maintain competitive performance but also exhibit resilience against adversarial attacks. Prioritizing accuracy alone may lead to overfitting or vulnerability to perturbations, while focusing solely on robustness could sacrifice overall classification performance. Finding an optimal balance between these two factors involves considering trade-offs based on compression ratios, training strategies, and evaluation metrics. Striking this balance ensures that distilled datasets are both accurate for standard tasks and secure against potential threats.

How can incorporating distilled data into training batches impact long-term model performance?

Incorporating distilled data into training batches offers several benefits for long-term model performance. Firstly, it introduces additional diversity to the training process by augmenting original samples with synthetic ones, potentially reducing overfitting tendencies associated with limited data availability. Secondly, including distilled data enhances model generalization by exposing it to a broader range of features present in both real and synthesized images. This exposure can improve the model's ability to handle unseen scenarios effectively while maintaining consistency across different tasks or domains. Overall, integrating distilled data into training batches contributes to enhanced adaptability and robustness in models over time.