  Imperceptible digital watermarking is important in copyright protection,
misinformation prevention, and responsible generative AI. We propose TrustMark
- a GAN-based watermarking method with novel design in architecture and
spatio-spectra losses to balance the trade-off between watermarked image
quality with the watermark recovery accuracy. Our model is trained with
robustness in mind, withstanding various in- and out-place perturbations on the
encoded image. Additionally, we introduce TrustMark-RM - a watermark remover
method useful for re-watermarking. Our methods achieve state-of-art performance
on 3 benchmarks comprising arbitrary resolution images.
