SimCert: Probabilistic Certification for Behavioral Similarity in Deep Neural Network Compression

• URL: http://arxiv.org/abs/2603.14818v1
• Date: Mon, 16 Mar 2026 04:44:37 GMT
• Title: SimCert: Probabilistic Certification for Behavioral Similarity in Deep Neural Network Compression
• Abstract summary: We propose SimCert, a probabilistic certification framework for verifying the behavioral similarity of compressed neural networks.<n>Our framework features: (1) A dual-network symbolic propagation method supporting both quantization and pruning; (2) A variance-aware bounding technique using Bernstein's inequality to tighten safety certificates; and (3) An automated verification toolchain.
• Score: 10.808953992870954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying Deep Neural Networks (DNNs) on resource-constrained embedded systems requires aggressive model compression techniques like quantization and pruning. However, ensuring that the compressed model preserves the behavioral fidelity of the original design is a critical challenge in the safety-critical system design flow. Existing verification methods often lack scalability or fail to handle the architectural heterogeneity introduced by pruning. In this work, we propose SimCert, a probabilistic certification framework for verifying the behavioral similarity of compressed neural networks. Unlike worst-case analysis, SimCert provides quantitative safety guarantees with adjustable confidence levels. Our framework features: (1) A dual-network symbolic propagation method supporting both quantization and pruning; (2) A variance-aware bounding technique using Bernstein's inequality to tighten safety certificates; and (3) An automated verification toolchain. Experimental results on ACAS Xu and computer vision benchmarks demonstrate that SimCert outperforms state-of-the-art baselines.

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