Improving Adversarial Robustness of Diffusion-Based Purification Models through Adversarial Denoising Diffusion Training

Core Concepts

Stochasticity in both the forward and reverse diffusion processes is the key factor driving the robustness of diffusion-based purification (DBP) models. To improve their robustness, Adversarial Denoising Diffusion Training (ADDT) equips DBP models with the ability to directly counter adversarial perturbations.

Abstract

The paper re-examines the robustness of diffusion-based purification (DBP) models, which use diffusion models to remove adversarial noise from images. Previous studies have used questionable methods to evaluate DBP robustness and lack experimental support for their explanations.
The authors first introduce a more rigorous evaluation framework that addresses the issues in prior work. Their re-evaluation shows that stochasticity has a significant impact on DBP robustness. To further investigate this, the authors propose a novel "Deterministic White-box" attack setting, where the attacker has full knowledge of the model's stochastic elements. The results confirm that stochasticity in both the forward and reverse diffusion processes is the key factor driving DBP robustness, rather than just the forward process as previously believed.
The authors then identify a potential limitation of DBP models - unlike adversarial training (AT) models, diffusion models may inherently lack the ability to directly counter adversarial perturbations. Instead, they rely primarily on stochastic elements to circumvent the most effective attack vectors.
To address this, the authors propose Adversarial Denoising Diffusion Training (ADDT), a novel approach that equips DBP models with the ability to directly counter adversarial perturbations. ADDT uses Rank-Based Gaussian Mapping (RBGM) to make adversarial perturbations compatible with the diffusion framework, and Classifier-Guided Perturbation Optimization (CGPO) to generate adversarial perturbations under the guidance of a pre-trained classifier. Empirical results show that ADDT significantly improves the robustness of DBP models, and further experiments confirm that it gives the models the ability to directly counter adversarial perturbations.

Stats

The paper provides the following key metrics and figures:

Clean accuracy of WideResNet-28-10 classifier on CIFAR-10: 95.12%
Robust accuracy of various DBP models under PGD20-EoT10 attack:

DPDDPM: 47.27% (l∞), 69.34% (l2)
DPDDIM: 42.19% (l∞), 70.02% (l2)
DiffPure: 55.96% (l∞), 75.78% (l2)
DPEDM: 62.50% (l∞), 76.86% (l2)

Robust accuracy of ADDT fine-tuned DBP models:

DPDDPM: 51.46% (l∞), 70.12% (l2)
DPDDIM: 46.48% (l∞), 71.19% (l2)
DiffPure: 62.11% (l∞), 76.66% (l2)
DPEDM: 66.41% (l∞), 79.16% (l2)

Quotes

"Our results suggest that DBP models rely on stochasticity to evade the most effective attack direction, rather than directly countering adversarial perturbations."
"To improve the robustness of DBP models against adversarial perturbations, we propose Adversarial Denoising Diffusion Training (ADDT)."

Key Insights Distilled From

Towards Better Adversarial Purification via Adversarial Denoising Diffusion Training

by Yiming Liu,K... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14309.pdf

Deeper Inquiries

What other techniques could be explored to further improve the robustness of diffusion-based purification models beyond relying on stochasticity

To further enhance the robustness of diffusion-based purification models beyond relying solely on stochasticity, several techniques could be explored:

Adaptive Noise Addition: Instead of using a fixed Gaussian noise level during the diffusion process, adaptive noise addition techniques could be implemented. This approach would dynamically adjust the noise level based on the input data characteristics, potentially improving the model's ability to counter adversarial perturbations.

Ensemble Learning: Employing ensemble learning by combining multiple diffusion models trained with different initializations or architectures could enhance robustness. By aggregating predictions from diverse models, the ensemble can provide more robust and reliable results.

Regularization Techniques: Incorporating regularization methods such as dropout, weight decay, or adversarial training during the training of diffusion models can help prevent overfitting and improve generalization, thereby enhancing robustness against adversarial attacks.

Feature Space Transformation: Exploring techniques that transform the input data into a different feature space before applying the diffusion process could potentially introduce additional layers of defense against adversarial perturbations.

Dynamic Perturbation Generation: Implementing dynamic perturbation generation strategies that adapt to the specific characteristics of the input data and the model's vulnerabilities could further improve the model's robustness.

How might the insights from this work on adversarial robustness apply to other generative modeling approaches beyond diffusion models

The insights gained from this work on adversarial robustness in diffusion-based purification models can be applied to other generative modeling approaches, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).

GANs: Similar to diffusion models, GANs can benefit from understanding the role of stochasticity in adversarial robustness. Techniques like adversarial denoising training and precise gradient evaluation can be adapted to enhance the robustness of GANs against adversarial attacks.

VAEs: Insights on the impact of stochasticity on model robustness can guide the development of more resilient VAEs. By incorporating techniques like Classifier-Guided Perturbation Optimization and Rank-Based Gaussian Mapping, VAEs can be trained to better counter adversarial perturbations.

By applying the principles and methodologies derived from diffusion-based purification models to other generative modeling approaches, researchers can advance the field of adversarial defense and improve the overall security of neural networks.

What are the potential real-world applications and implications of improving the adversarial robustness of diffusion-based purification models

The improvement in adversarial robustness of diffusion-based purification models has significant real-world applications and implications across various domains:

Cybersecurity: Enhanced adversarial robustness in diffusion-based purification models can bolster the security of AI systems used in cybersecurity applications, such as intrusion detection, malware detection, and threat analysis.

Healthcare: In the healthcare sector, robust diffusion models can be utilized for medical image analysis, disease diagnosis, and patient monitoring, ensuring the reliability and integrity of AI-driven healthcare solutions.

Autonomous Vehicles: Improved adversarial robustness in diffusion models can enhance the safety and reliability of autonomous vehicles by mitigating the risks of adversarial attacks that could compromise the vehicle's decision-making processes.

Finance: In the financial industry, robust diffusion models can be employed for fraud detection, risk assessment, and algorithmic trading, safeguarding financial systems against adversarial manipulation and attacks.

By fortifying diffusion-based purification models against adversarial threats, these advancements can lead to more secure and trustworthy AI applications, benefiting society in various critical areas.