A Parallel Optimization Method for Robustness Verification of Deep Neural Networks

Renhao Lin, Qinglei Zhou (), Xiaofei Nan and Tianqing Hu
Additional contact information
Renhao Lin: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Qinglei Zhou: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Xiaofei Nan: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Tianqing Hu: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Mathematics, 2024, vol. 12, issue 12, 1-19

Abstract: Deep neural networks (DNNs) have gained considerable attention for their expressive capabilities, but unfortunately they have serious robustness risks. Formal verification is an important technique to ensure network reliability. However, current verification techniques are unsatisfactory in time performance, which hinders the practical applications. To address this issue, we propose an efficient optimization method based on parallel acceleration with more computing resources. The method involves the speedup configuration of a partition-based verification aligned with the structures and robustness formal specifications of DNNs. A parallel verification framework is designed specifically for neural network verification systems, which integrates various auxiliary modules and accommodates diverse verification modes. The efficient parallel scheduling of verification queries within the framework enhances resource utilization and enables the system to process a substantial volume of verification tasks. We conduct extensive experiments on multiple commonly used verification benchmarks to demonstrate the rationality and effectiveness of the proposed method. The results show that higher efficiency is achieved after parallel optimization integration.

Keywords: deep neural networks; robustness verification; parallel acceleration; partition mode; task scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/12/1884/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/12/1884/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:12:p:1884-:d:1416498

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().