23 research outputs found
Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations
The training and creation of deep learning model is usually costly, thus it
can be regarded as an intellectual property (IP) of the model creator. However,
malicious users who obtain high-performance models may illegally copy,
redistribute, or abuse the models without permission. To deal with such
security threats, a few deep neural networks (DNN) IP protection methods have
been proposed in recent years. This paper attempts to provide a review of the
existing DNN IP protection works and also an outlook. First, we propose the
first taxonomy for DNN IP protection methods in terms of six attributes:
scenario, mechanism, capacity, type, function, and target models. Then, we
present a survey on existing DNN IP protection works in terms of the above six
attributes, especially focusing on the challenges these methods face, whether
these methods can provide proactive protection, and their resistances to
different levels of attacks. After that, we analyze the potential attacks on
DNN IP protection methods from the aspects of model modifications, evasion
attacks, and active attacks. Besides, a systematic evaluation method for DNN IP
protection methods with respect to basic functional metrics, attack-resistance
metrics, and customized metrics for different application scenarios is given.
Lastly, future research opportunities and challenges on DNN IP protection are
presented
PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models
Deep neural networks (DNNs) have achieved tremendous success in artificial
intelligence (AI) fields. However, DNN models can be easily illegally copied,
redistributed, or abused by criminals, seriously damaging the interests of
model inventors. The copyright protection of DNN models by neural network
watermarking has been studied, but the establishment of a traceability
mechanism for determining the authorized users of a leaked model is a new
problem driven by the demand for AI services. Because the existing traceability
mechanisms are used for models without watermarks, a small number of
false-positives are generated. Existing black-box active protection schemes
have loose authorization control and are vulnerable to forgery attacks.
Therefore, based on the idea of black-box neural network watermarking with the
video framing and image perceptual hash algorithm, a passive copyright
protection and traceability framework PCPT is proposed that uses an additional
class of DNN models, improving the existing traceability mechanism that yields
a small number of false-positives. Based on an authorization control strategy
and image perceptual hash algorithm, a DNN model active copyright protection
and traceability framework ACPT is proposed. This framework uses the
authorization control center constructed by the detector and verifier. This
approach realizes stricter authorization control, which establishes a strong
connection between users and model owners, improves the framework security, and
supports traceability verification