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.
Content-Based Image Copy Detection Using Convolutional Neural Network
