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SafeDeep: A Scalable Robustness Verification Framework for Deep Neural Networks

Baninajjar, Anahita LU orcid ; Hosseini, Kamran ; Rezine, Ahmed and Aminifar, Amir LU orcid (2023) IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2023
Abstract
The state-of-the-art machine learning techniques come with limited, if at all any, formal correctness guarantees. This has been demonstrated by adversarial examples in the deep learning domain. To address this challenge, here, we propose a scalable robustness verification framework for Deep Neural Networks (DNNs). The framework relies on Linear Programming (LP) engines and builds on decades of advances in the field for analyzing convex approximations of the original network. The key insight is in the on-demand incremental refinement of these convex approximations. This refinement can be parallelized, making the framework even more scalable. We have implemented a prototype tool to verify the robustness of a large number of DNNs in epileptic... (More)
The state-of-the-art machine learning techniques come with limited, if at all any, formal correctness guarantees. This has been demonstrated by adversarial examples in the deep learning domain. To address this challenge, here, we propose a scalable robustness verification framework for Deep Neural Networks (DNNs). The framework relies on Linear Programming (LP) engines and builds on decades of advances in the field for analyzing convex approximations of the original network. The key insight is in the on-demand incremental refinement of these convex approximations. This refinement can be parallelized, making the framework even more scalable. We have implemented a prototype tool to verify the robustness of a large number of DNNs in epileptic seizure detection. We have compared the results with those obtained by two state-of-the-art tools for the verification of DNNs. We show that our framework is consistently more precise than the over-approximation-based tool ERAN and more scalable than the SMT-based tool Reluplex. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
in press
subject
host publication
2023 IEEE International Conference on Acoustics, Speech and Signal Processing
conference name
IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2023
conference location
Rhodes Island, Greece
conference dates
2023-06-04 - 2023-06-10
external identifiers
  • scopus:85180553668
DOI
10.1109/ICASSP49357.2023.10097028
project
WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
language
English
LU publication?
yes
id
7c504e0f-72f6-4f87-961f-453803f3dd7c
date added to LUP
2023-03-30 15:23:35
date last changed
2024-02-08 15:38:59
@inproceedings{7c504e0f-72f6-4f87-961f-453803f3dd7c,
  abstract     = {{The state-of-the-art machine learning techniques come with limited, if at all any, formal correctness guarantees. This has been demonstrated by adversarial examples in the deep learning domain. To address this challenge, here, we propose a scalable robustness verification framework for Deep Neural Networks (DNNs). The framework relies on Linear Programming (LP) engines and builds on decades of advances in the field for analyzing convex approximations of the original network. The key insight is in the on-demand incremental refinement of these convex approximations. This refinement can be parallelized, making the framework even more scalable. We have implemented a prototype tool to verify the robustness of a large number of DNNs in epileptic seizure detection. We have compared the results with those obtained by two state-of-the-art tools for the verification of DNNs. We show that our framework is consistently more precise than the over-approximation-based tool ERAN and more scalable than the SMT-based tool Reluplex.}},
  author       = {{Baninajjar, Anahita and Hosseini, Kamran and Rezine, Ahmed and Aminifar, Amir}},
  booktitle    = {{2023 IEEE International Conference on Acoustics, Speech and Signal Processing}},
  language     = {{eng}},
  title        = {{SafeDeep: A Scalable Robustness Verification Framework for Deep Neural Networks}},
  url          = {{https://lup.lub.lu.se/search/files/141851887/SafeDeep.pdf}},
  doi          = {{10.1109/ICASSP49357.2023.10097028}},
  year         = {{2023}},
}