UTAF: A Universal Approach to Task-Agnostic Model Fingerprinting. (arXiv:2201.07391v1 [cs.CR])

In this paper, we present Universal Task-Agnostic Fingerprinting
(\textit{UTAF}), the first task-agnostic model fingerprinting framework which
enables fingerprinting on a much wider range of DNNs independent from the
downstream learning task, and exhibits strong robustness against a variety of
ownership obfuscation techniques. Specifically, we generalize previous schemes
into two critical design components in UTAF: the \textit{adaptive fingerprint}
and the \textit{meta-verifier}, which are jointly optimized such that the
meta-verifier learns to determine whether a suspect model is stolen based on
the …

fingerprinting task