Better Diffusion Models Further Improve Adversarial Training (2302.04638v2)

Abstract: It has been recognized that the data generated by the denoising diffusion probabilistic model (DDPM) improves adversarial training. After two years of rapid development in diffusion models, a question naturally arises: can better diffusion models further improve adversarial training? This paper gives an affirmative answer by employing the most recent diffusion model which has higher efficiency ($\sim 20$ sampling steps) and image quality (lower FID score) compared with DDPM. Our adversarially trained models achieve state-of-the-art performance on RobustBench using only generated data (no external datasets). Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70.69\%$ and $42.67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i.e. improving upon previous state-of-the-art models by $+4.58\%$ and $+8.03\%$. Under the $\ell_2$-norm threat model with $\epsilon=128/255$, our models achieve $84.86\%$ on CIFAR-10 ($+4.44\%$). These results also beat previous works that use external data. We also provide compelling results on the SVHN and TinyImageNet datasets. Our code is available at https://github.com/wzekai99/DM-Improves-AT.

• The paper shows that integrating EDM-based diffusion models in adversarial training improves robustness with +4.6% on CIFAR-10 and +8.0% on CIFAR-100.
• It leverages efficient EDM with only 20 sampling steps to generate high-quality synthetic data, reducing FID scores compared to prior models.
• Extensive experiments reveal that enriched synthetic data effectively mitigates robust overfitting, closing the generalization gap across multiple datasets.

An Academic Essay on "Better Diffusion Models Further Improve Adversarial Training"

This paper investigates the potential of advanced diffusion models to enhance adversarial training (AT). The authors focus specifically on the elucidating diffusion model (EDM), leveraging its improved efficiency and image quality to address notable limitations in prior work. Traditional adversarial training approaches have been effective in improving model robustness against adversarial attacks. However, these methods often require an extensive dataset, which is not always readily available. Recent advancements in diffusion models, particularly the denoising diffusion probabilistic model (DDPM), have shown promise by generating synthetic data to bolster AT.

Key Contributions

The authors of this paper evaluate whether the advancements in diffusion models, exemplified by the state-of-the-art EDM, can further enhance AT. EDM achieves superior efficiency with approximately 20 sampling steps and delivers high-quality images with lower FID scores compared to its predecessors. By integrating EDM-generated data into the adversarial training pipeline, the authors achieve state-of-the-art performance benchmarks on CIFAR-10 and CIFAR-100. Notably, their models exhibit robust accuracy improvements of +4.58% and +8.03% for these datasets, respectively, under the $\ell_\infty$-norm threat model with $\epsilon=8/255$. Additionally, significant performance gains are observed under the $\ell_2$-norm threat model and across other datasets such as SVHN and TinyImageNet.

Experimental Insights and Mechanistic Evaluations

The authors conduct extensive experiments to verify the efficacy of their approach. They compare various configurations of diffusion models, evaluate the effect of different sampling steps, and examine the role of generated data in mitigating robust overfitting—a common phenomenon in AT where the test robust loss increases with prolonged training. By doing so, the paper affirms that better quality and a larger quantity of generated data substantially contribute to closing the robust generalization gap. This enhanced data-generated process mitigates the overfitting problem, allowing models to attain superior robustness without external datasets.

Theoretical and Practical Implications

From a theoretical perspective, the work enhances our understanding of the link between diffusion models and adversarial robustness. The findings suggest a promising avenue, where improved generative models can serve as potent tools for adversarial defense. On a practical level, this approach empowers practitioners with more robust training strategies, especially in scenarios lacking additional labeled data.

Moreover, the authors' sensitivity analysis of various hyperparameters lends valuable insights into optimizing adversarial training workflows, affirming, for instance, the advantage of larger batch sizes and appropriate label smoothing adjustments when deploying robust models.

Future Prospects and Research Directions

This research opens several pathways for future exploration. One notable direction involves optimizing the computational efficiency of using diffusion models in AT. Current processes, albeit effective, entail substantial computational costs, either in the data generation or inference stages. Strategies such as reducing sample complexity or integrating real-time generative techniques could prove fruitful. Moreover, exploring the synergy between diffusion models and alternative adversarial defense mechanisms could yield holistic improvement strategies.

In conclusion, this paper demonstrates the significant advantages of utilizing advanced diffusion models like EDM in the context of adversarial training. The profound improvements in robust accuracy and insights into overfitting mechanisms underscore the potential that lies in the intersection of generative modeling and adversarial robustness. This work not only sets a new benchmark for adversarial training but also paves the way for innovative research and applications in the field.