End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery
[Kryptografia i bezpieczeństwo] Autorzy: Akira Ito, Takayuki Miura, Yosuke Todo
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, requires a large number of queries and substantial computation. Implementing this step in a black-box setting remains difficult. Consequently, a fully black-box end-to-end demonstration on trained deep ReLU MLPs has remained a challenge. In this paper, we propose a new sign-recovery algorithm bas…
- Kategoria arxiv
- cs.CR
- Kategoria
- Kryptografia i bezpieczeństwo