  The onboarding of IoT devices by authorized users constitutes both a
challenge and a necessity in a world, where the number of IoT devices and the
tampering attacks against them continuously increase. Commonly used onboarding
techniques today include the use of QR codes, pin codes, or serial numbers.
These techniques typically do not protect against unauthorized device access-a
QR code is physically printed on the device, while a pin code may be included
in the device packaging. As a result, any entity that has physical access to a
device can onboard it onto their network and, potentially, tamper it
(e.g.,install malware on the device). To address this problem, in this paper,
we present a framework, called Deep Learning-based Watermarking for authorized
IoT onboarding (DLWIoT), featuring a robust and fully automated image
watermarking scheme based on deep neural networks. DLWIoT embeds user
credentials into carrier images (e.g., QR codes printed on IoT devices), thus
enables IoT onboarding only by authorized users. Our experimental results
demonstrate the feasibility of DLWIoT, indicating that authorized users can
onboard IoT devices with DLWIoT within 2.5-3sec.
