SOURCE-LINKED INTELLIGENCE

End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

The importance of deep neural networks (DNNs) is widely recognized, and the parameters obtained through training are regarded as valuable assets. Recently, attacks that extract these parameters using only oracle queries to a DNN have been actively studied at IACR conferences. The hard-label setting is the most challenging setting for model extraction, where an adversary can observe only the final output label, such as "dog" or "cat." At Eurocrypt 2025, Carlini et al. proposed polynomial-time hard-label extraction of ReLU-based MLPs. However, one step of this attack process, i.e., sign recovery

Read original source ↗ Open in workspace

recordType

paper

region

Global