Convolutional Neural Network Copy Detection with Neural Network
In recent years, many model intellectual property (IP) proof methods for
IP protection have been proposed, such as model watermarking and model
fingerprinting. However, as an important part of the model IP protection
system, the model copy detection task has not received enough attention.
With the increasing number of neural network models transmitted and
deployed on the Internet, the search for similar models is in great
demand, which concurrently triggers the security problem of copy
detection of models for IP protection. Due to the high computational
complexity, both model watermarking and model fingerprinting lack the
capability to efficiently find suspected infringing models among tens of
millions of models. In this paper, inspired by the hash-based image
retrieval methods, we propose a perceptual hashing algorithm for
convolutional neural networks (CNNs). The proposed perceptual hashing
algorithm can convert the weights of CNNs to fixed-length binary hash
codes so that the lightly modified version has the similar hash code as
the original model. By comparing the similarity of a pair of hash codes
between a query model and a test model in the model library, the similar
versions of a query model can be retrieved efficiently. To the best of
our knowledge, this is the first perceptual hashing algorithm for CNNs.
The experiment performed on a model library containing 3,565 models
indicates that our proposed perceptual hashing scheme has a superior
copy detection performance.