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.