Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation
Luca Marzari, Isabella Mastroeni, Alessandro Farinelli·May 08, 2025
Summary
ABSTRACT DNN-VERIFICATION uses abstract interpretation for deep neural network formal verification, ranking adversarial inputs to enhance model safety. It surpasses current methods with a hierarchical structure for tolerable unsafe outputs, classifying models as 'abstract safe'. An algorithm computes potential answers in abstracted DNN semantics, focusing on maximal overlapping output intervals. Empirical evaluation using the Habitat-Lab simulator for training deep reinforcement learning models is presented. A hierarchical abstraction technique is applied to the agent's continuous output space, dividing it into discrete velocity classes for more manageable verification.
Introduction
Background
Overview of deep neural networks (DNNs) and their applications
Challenges in formal verification of DNNs
Importance of formal verification in ensuring model safety
Objective
To introduce ABSTRACT DNN-VERIFICATION, a novel approach for formal verification of DNNs
To present the hierarchical structure for handling tolerable unsafe outputs
To demonstrate the classification of models as 'abstract safe'
Method
Data Collection
Gathering datasets for training and testing DNNs
Selection of deep reinforcement learning models for evaluation
Data Preprocessing
Preparation of adversarial inputs for formal verification
Transformation of continuous output spaces into discrete classes
Algorithm
Computation of potential answers in abstracted DNN semantics
Focus on maximal overlapping output intervals for efficient verification
Hierarchical Abstraction
Application of a hierarchical abstraction technique
Division of the agent's continuous output space into discrete velocity classes
Empirical Evaluation
Habitat-Lab Simulator
Description of the Habitat-Lab simulator
Use in training deep reinforcement learning models
Evaluation Metrics
Criteria for assessing the effectiveness of ABSTRACT DNN-VERIFICATION
Results
Presentation of empirical results from the evaluation
Comparison with current methods in formal verification
Conclusion
Summary of Contributions
Recap of the main findings and contributions of ABSTRACT DNN-VERIFICATION
Future Work
Discussion of potential areas for further research and development
Impact
Implications of the approach for enhancing the safety and reliability of DNNs in real-world applications
Basic info
papers
artificial intelligence
Advanced features
Insights
How does the hierarchical structure in DNN-VERIFICATION improve model safety?
What are the potential limitations of using hierarchical abstraction in DNN-VERIFICATION?
What is the primary goal of DNN-VERIFICATION as described in the abstract?
What innovative techniques does DNN-VERIFICATION introduce for handling adversarial inputs?