  Deep learning has shown great promise in the domain of medical image
analysis. Medical professionals and healthcare providers have been adopting the
technology to speed up and enhance their work. These systems use deep neural
networks (DNN) which are vulnerable to adversarial samples; images with
imperceivable changes that can alter the model's prediction. Researchers have
proposed defences which either make a DNN more robust or detect the adversarial
samples before they do harm. However, none of these works consider an informed
attacker which can adapt to the defence mechanism. We show that an informed
attacker can evade five of the current state of the art defences while
successfully fooling the victim's deep learning model, rendering these defences
useless. We then suggest better alternatives for securing healthcare DNNs from
such attacks: (1) harden the system's security and (2) use digital signatures.
