  Deep neural networks have had enormous impact on various domains of computer
science, considerably outperforming previous state of the art machine learning
techniques. To achieve this performance, neural networks need large quantities
of data and huge computational resources, which heavily increases their
construction costs. The increased cost of building a good deep neural network
model gives rise to a need for protecting this investment from potential
copyright infringements. Legitimate owners of a machine learning model want to
be able to reliably track and detect a malicious adversary that tries to steal
the intellectual property related to the model. Recently, this problem was
tackled by introducing in deep neural networks the concept of watermarking,
which allows a legitimate owner to embed some secret information(watermark) in
a given model. The watermark allows the legitimate owner to detect copyright
infringements of his model. This paper focuses on verifying the robustness and
reliability of state-of- the-art deep neural network watermarking schemes. We
show that, a malicious adversary, even in scenarios where the watermark is
difficult to remove, can still evade the verification by the legitimate owners,
thus avoiding the detection of model theft.
