  It is crucial to protect the intellectual property rights of DNN models prior
to their deployment. The DNN should perform two main tasks: its primary task
and watermarking task. This paper proposes a lightweight, reliable, and secure
DNN watermarking that attempts to establish strong ties between these two
tasks. The samples triggering the watermarking task are generated using image
Mixup either from training or testing samples. This means that there is an
infinity of triggers not limited to the samples used to embed the watermark in
the model at training. The extensive experiments on image classification models
for different datasets as well as exposing them to a variety of attacks, show
that the proposed watermarking provides protection with an adequate level of
security and robustness.
