In this paper, we present and analyze two novel - and seem- ingly distant - research trends in Machine Learning: ML wa- termarking and adversarial patches. First, we show how ML watermarking uses specially crafted inputs to provide a proof of model ownership. Second, we demonstrate how an attacker can craft adversarial samples in order to trigger an abnormal behavior in a model and thus perform an ambiguity attack on ML watermarking. Finally, we describe three countermea- sures that could be applied in order to prevent ambiguity at- tacks. We illustrate our works using the example of a binary classification model for welding inspection.