Watermarking is an operation of embedding infor-
mation into an image in a way that allows to identify ownership
of the image despite applying some distortions on it. In this
paper, we present a novel end-to-end solution for embedding and
recovering the watermark in the digital image using convolutional
neural networks. We propose a spreading method of the message
over the spatial domain of the image, hence reducing the local
bits per pixel capacity and significantly increasing robustness. To
obtain the model we use adversarial training, apply noiser layers
between the encoder and the decoder, and implement a precise
JPEG approximation. Moreover, we broaden the spectrum of
typically considered attacks on the watermark and we achieve
high overall robustness, most notably against JPEG compression,
Gaussian blur, subsampling or resizing. We show that an appli-
cation of some attacks could increase robustness against other
non-seen during training distortions across one group of attacks
— a proper grouping of the attacks according to their scope
allows to achieve high general robustness