首次实现基于锚框的目标检测器形式化鲁棒性验证。
IoUCert: Robustness Verification for Anchor-based Object Detectors
- 提出新坐标变换方法，绕过非线性框预测的精度损失。
- 通过区间传播法，精确计算目标检测的交并比（IoU）上界。
- 可验证SSD、YOLOv2/3等主流模型在扰动下的鲁棒性。
尽管形式化鲁棒性验证在图像分类中已取得显著进展，但将其推广至目标检测仍面临巨大挑战，主要源于复杂的非线性坐标变换和交并比（IoU）度量。为实现对完整检测流程的验证，本文提出IoUCert，一种专为克服核心数学瓶颈设计的形式化验证框架。通过在单目标场景下分离定位任务，我们引入一种坐标变换，避免了非线性框预测函数带来的精度下降问题。该方法允许直接针对锚框偏移优化边界，从而提出一种新型区间传播算法，用于推导最优的IoU边界。实验表明，IoUCert首次实现了对SSD、YOLOv2和YOLOv3等基础锚框架构的鲁棒性验证，可抵御多种输入扰动，为未来端到端检测器的理论验证奠定坚实基础。
原文摘要 · Abstract (English)
While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics. As a fundamental step towards verifying complete detection pipelines, we introduce IoUCert, a novel formal verification framework designed specifically to overcome these core mathematical bottlenecks. By isolating the object localisation task in single-object settings, we propose a coordinate transformation that circumvents precision-degrading relaxations of non-linear box prediction functions. This approach allows us to optimise bounds directly with respect to anchor box offsets, enabling a novel Interval Bound Propagation method that derives optimal IoU bounds. We demonstrate that IoUCert enables, for the first time, the robustness verification of foundational, anchor-based architectures including tractable variants of SSD, YOLOv2, and YOLOv3 against various input perturbations, providing a rigorous theoretical basis for future end-to-end detector verification.
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