  Backdoor-based watermarking schemes were proposed to protect the intellectual
property of artificial intelligence models, especially deep neural networks,
under the black-box setting. Compared with ordinary backdoors, backdoor-based
watermarks need to digitally incorporate the owner's identity, which fact adds
extra requirements to the trigger generation and verification programs.
Moreover, these concerns produce additional security risks after the
watermarking scheme has been published for as a forensics tool or the owner's
evidence has been eavesdropped on. This paper proposes the capsulation attack,
an efficient method that can invalidate most established backdoor-based
watermarking schemes without sacrificing the pirated model's functionality. By
encapsulating the deep neural network with a rule-based or Bayes filter, an
adversary can block ownership probing and reject the ownership verification. We
propose a metric, CAScore, to measure a backdoor-based watermarking scheme's
security against the capsulation attack. This paper also proposes a new
backdoor-based deep neural network watermarking scheme that is secure against
the capsulation attack by reversing the encoding process and randomizing the
exposure of triggers.
