Efficient Incremental Certification of Robustness for Approximate Deep Neural Networks

Conceitos Básicos

This work presents the first approach for incremental robustness certification of deep neural networks using randomized smoothing. It significantly reduces the computational cost of certifying modified DNNs while maintaining strong robustness guarantees.

Resumo

The paper presents Incremental Randomized Smoothing (IRS), a novel approach for efficiently certifying the robustness of approximate deep neural networks (DNNs).
Key highlights:

Randomized smoothing is an effective approach for obtaining robustness certificates of DNNs, but it is computationally expensive, especially when certifying with a large number of samples.
When the smoothed model is modified (e.g., quantized or pruned), certification guarantees may not hold for the modified DNN, and recertifying from scratch can be prohibitively expensive.
IRS leverages the certification guarantees obtained from the original smoothed model to efficiently certify an approximated model with very few samples.
IRS is based on three key insights: (1) common approximations yield small disparity between the original and approximate models, (2) estimating this disparity requires fewer samples, and (3) this disparity can be used to soundly reuse the samples from certifying the original model.
Extensive experiments on CIFAR-10 and ImageNet datasets demonstrate that IRS can achieve up to 4.1x speedup over the standard non-incremental randomized smoothing baseline, while maintaining strong robustness guarantees.

Estatísticas

Randomized smoothing requires DNN inference on a large number of corruptions per input, which can be computationally expensive.
For int8 quantization of ResNet-110 on CIFAR-10, IRS reduced the certification time from 2.91 hours (baseline) to 0.71 hours, resulting in time savings of 2.12 hours (4.1x faster).
For int8 quantization of ResNet-50 on ImageNet, IRS reduced the certification time from 27.82 hours (baseline) to 22.45 hours, saving approximately 5.36 hours (1.24x faster).

Citações

"Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks."
"When the smoothed model is modified (e.g., quantized or pruned), certification guarantees may not hold for the modified DNN, and recertifying from scratch can be prohibitively expensive."

Principais Insights Extraídos De

Incremental Randomized Smoothing Certification

by Shubham Ugar... às arxiv.org 04-12-2024

https://arxiv.org/pdf/2305.19521.pdf

Perguntas Mais Profundas

How can the IRS approach be extended to handle other types of DNN approximations beyond quantization and pruning

The IRS approach can be extended to handle other types of DNN approximations beyond quantization and pruning by adapting the certification process to accommodate the specific characteristics of each approximation technique. For example:

Weight Sharing: For weight sharing techniques, IRS can reuse the certification guarantees of the original network to certify the smoothed approximated model with shared weights. By considering the impact of weight sharing on the robustness guarantees, IRS can adjust the estimation of disparity and certified radius accordingly.
Knowledge Distillation: When using knowledge distillation to approximate a DNN, IRS can leverage the information from the teacher network to certify the student network. This involves incorporating the knowledge transfer process into the certification framework to ensure the robustness of the distilled model.
Architecture Pruning: In the case of architecture pruning, IRS can adapt the certification process to account for the structural changes in the pruned network. By considering the sparsity introduced by pruning, IRS can adjust the estimation of disparity and certified radius to reflect the reduced network complexity.

By customizing the IRS algorithm to suit the specific characteristics of different DNN approximations, such as weight sharing, knowledge distillation, and architecture pruning, the approach can be extended to handle a wide range of approximation techniques effectively.

Can IRS be integrated with other randomized smoothing extensions, such as those that use different noise distributions or handle different types of perturbations

Integrating IRS with other randomized smoothing extensions, such as those using different noise distributions or handling different types of perturbations, can enhance the robustness certification process for DNNs. Here's how IRS can be integrated with these extensions:

Different Noise Distributions: IRS can incorporate variations in noise distributions by adjusting the estimation of disparity and certified radius based on the characteristics of the specific noise distribution used. By considering the impact of different noise distributions on the robustness guarantees, IRS can provide more comprehensive certification results.
Handling Different Perturbations: Extensions of randomized smoothing that handle different types of perturbations, such as geometric transformations or adversarial attacks, can be integrated with IRS by adapting the certification process to account for these perturbations. IRS can reuse the certification guarantees from the original network to certify the smoothed approximated model under various perturbation scenarios, ensuring robustness across different types of attacks.

By integrating IRS with these extensions, DNNs can be certified against a broader range of threats and uncertainties, enhancing their reliability and trustworthiness in real-world applications.

What are the potential applications of the IRS approach beyond DNN certification, such as in the context of iterative model development and deployment

The IRS approach has potential applications beyond DNN certification, particularly in the context of iterative model development and deployment. Some of the key applications include:

Iterative Model Improvement: IRS can be used to streamline the iterative process of improving DNN models by providing efficient and effective certification of modified networks. This allows researchers and practitioners to quickly assess the robustness of updated models and make informed decisions for further enhancements.
Dynamic Model Deployment: In dynamic deployment scenarios where DNNs are continuously updated or adapted to changing environments, IRS can ensure the ongoing robustness certification of evolving models. By incrementally certifying modified networks, IRS supports the seamless integration of updated models into operational systems.
Adaptive System Security: Beyond DNNs, IRS can be applied to certify the robustness of various machine learning models and algorithms used in adaptive security systems. By providing incremental certification for evolving models, IRS enhances the security posture of dynamic systems and safeguards against emerging threats.

Overall, the IRS approach offers a versatile framework for ensuring the robustness of evolving models and systems, making it valuable in diverse applications beyond DNN certification.