Deep neural networks have been leveraged in surprising ways in the context of computational inverse problems and imaging over the past few years. In this talk, I will explain how deep nets can sometimes generate helpful virtual "deepfake" data that were not originally recorded, but which extend the reach of inversion in a variety of ways. I will discuss two examples from seismic imaging: 1) bandwidth extension, which helps to convexify the inverse problem, and 2) "physics swap", which helps to mitigate nuisance parameters. Joint work with Hongyu Sun, Pawan Bharadwaj, and Matt Li.