House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Bibtex Paper Supplemental


Xudong Pan, Shengyao Zhang, Mi Zhang, Yifan Yan, Min Yang


In this paper, we present a capacity-aware neuron steganography scheme (i.e., Cans) to covertly transmit multiple private machine learning (ML) datasets via a scheduled-to-publish deep neural network (DNN) as the carrier model. Unlike existing steganography schemes which treat the DNN parameters as bit strings, \textit{Cans} for the first time exploits the learning capacity of the carrier model via a novel parameter sharing mechanism. Extensive evaluation shows, Cans is the first working scheme which can covertly transmit over $10000$ real-world data samples within a carrier model which has $220\times$ less parameters than the total size of the stolen data, and simultaneously transmit multiple heterogeneous datasets within a single carrier model, under a trivial distortion rate ($<10^{-5}$) and with almost no utility loss on the carrier model ($<1\%$). Besides, Cans implements by-design redundancy to be resilient against common post-processing techniques on the carrier model before the publishing.