ATKVerifier: Adaptive Top-K Constraints for Tighter Verification of Semantic Segmentation Networks

Abstract

Abstract Formal verification of Semantic Segmentation Networks is challenging due to high-dimensional output spaces and cumulative over-approximation errors in deep architectures. Existing verification methods based on specific Star-set reachability suffer from either exponential state explosion (exact splitting) or excessive conservativeness (interval-based relaxation). In this work, we present ATKVerifier , a verification framework for SSNs operating on an abstract domain named constrained-star (C-star), which captures spatial dependencies within MaxPool receptive fields through explicit predicate constraints. Our framework features: (1) an adaptive top-K lower bound mechanism that dynamically encodes K potential maximizers based on layer depth and interval overlap, balancing precision and computational cost through parameter-free adaptation; (2) an adaptive affine upper bound exploiting linear relationships between top candidates to replace conservative constant bounds; and (3) region-level completeness (RLC), a spatial robustness metric quantifying the integrity of verified contiguous object regions. Experiments on M2NIST with three SSN architectures (16 $$\sim $$ ∼ 24 layers) demonstrate 8 $$\sim $$ ∼ 25% improvement in robust Intersection-over-Union (IoU) over the ImageStar-based NNV baseline, with the improvement scaling with the network’s depth. For the 24-layer architecture, ATKVerifier achieves 59.2% RLC versus 43.8% of NNV, certifying 35.2% more complete semantic objects.