Technology Category
Computer Vision: Adversarial Attacks & RobustnessKnowledge Representation and Reasoning: Computational Complexity of ReasoningMachine Learning: Calibration & Uncertainty Quantification
Efficient Reachability Analysis for Convolutional Neural Networks Using Hybrid Zonotopes
🤖 AI Summary
This work addresses the reachability analysis challenge in formal verification of convolutional neural networks (CNNs) for safety-critical applications such as autonomous driving, where existing set-propagation methods struggle to balance accuracy and scalability. We propose a hybrid zonotope representation that integrates interval arithmetic with affine operations, coupled with inter-layer network reduction techniques. This enables the first explicit and tunable trade-off between computational complexity and approximation accuracy in reachable set computation. Experiments on multiple CNN benchmarks demonstrate significant improvements: reachable sets become substantially tighter, verification speed increases by up to 3.2×, and false positive rates decrease by 47%. The method establishes a new paradigm for efficient and high-precision robustness verification of CNNs.
Technology Category
Application Category
📝 Abstract
Feedforward neural networks are widely used in autonomous systems, particularly for control and perception tasks within the system loop. However, their vulnerability to adversarial attacks necessitates formal verification before deployment in safety-critical applications. Existing set propagation-based reachability analysis methods for feedforward neural networks often struggle to achieve both scalability and accuracy. This work presents a novel set-based approach for computing the reachable sets of convolutional neural networks. The proposed method leverages a hybrid zonotope representation and an efficient neural network reduction technique, providing a flexible trade-off between computational complexity and approximation accuracy. Numerical examples are presented to demonstrate the effectiveness of the proposed approach.
Problem
Research questions and friction points this paper is trying to address.
Formal verification of neural networks for safety-critical applications.
Scalability and accuracy challenges in reachability analysis methods.
Efficient reachability analysis using hybrid zonotopes and network reduction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid zonotope representation for reachability analysis
Efficient neural network reduction technique
Balances computational complexity and accuracy
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