  Advancements in deep generative models have made it possible to synthesize
images, videos and audio signals that are difficult to distinguish from natural
signals, creating opportunities for potential abuse of these capabilities. This
motivates the problem of tracking the provenance of signals, i.e., being able
to determine the original source of a signal. Watermarking the signal at the
time of signal creation is a potential solution, but current techniques are
brittle and watermark detection mechanisms can easily be bypassed by applying
post-processing transformations (cropping images, shifting pitch in the audio
etc.). In this paper, we introduce ReSWAT (Resilient Signal Watermarking via
Adversarial Training), a framework for learning transformation-resilient
watermark detectors that are able to detect a watermark even after a signal has
been through several post-processing transformations. Our detection method can
be applied to domains with continuous data representations such as images,
videos or sound signals. Experiments on watermarking image and audio signals
show that our method can reliably detect the provenance of a signal, even if it
has been through several post-processing transformations, and improve upon
related work in this setting. Furthermore, we show that for specific kinds of
transformations (perturbations bounded in the L2 norm), we can even get formal
guarantees on the ability of our model to detect the watermark. We provide
qualitative examples of watermarked image and audio samples in
https://drive.google.com/open?id=1-yZ0WIGNu2Iez7UpXBjtjVgZu3jJjFga.
