Paper 2026/902

End-to-End Polynomial-Time Cryptanalytic Extraction of Convolutional Neural Networks in the Hard-Label Setting

Abstract

Convolutional neural network parameters are valuable intellectual property, yet many APIs expose only top-1 labels and assume hidden logits limit parameter recovery. Prior cryptanalytic extraction can recover functionally equivalent ReLU MLPs, but CNNs introduce weight sharing, parallel critical hyperplanes, coupled spatial perturbations, and channel-sign ambiguity. This paper presents an end-to-end hard-label extraction attack for known-architecture ReLU CNN classifiers with average pooling. The main algorithmic contribution is channel-level recovery with SVGR-guided retained-candidate discrete optimization under a retained-candidate assumption. The attack locates dual points on decision and activation boundaries, recovers shared channel signatures with SVD, resolves channel signs, and peels layers while absorbing ReLU scale factors into later linear layers. Across evaluated 1D MNIST, 2D MNIST, and RGB CIFAR-10 variants, extraction reaches 100% prediction fidelity. Moreover, the evaluation demonstrates downstream security implications: extracted watermarked CNNs preserve behavior-level ownership evidence. Furthermore, the recovered models can be wrapped with deterministic triggers without erasing retained watermark signals, creating risk for both owners and downstream users. These results demonstrate that hiding logits alone does not protect parameters for this CNN family once architecture information is available. The anonymous artifact is available for review at https://anonymous.4open.science/r/cnn_hard_label_extraction-83F4.