Watermarking Images in Self-Supervised Latent Spaces

Pierre Fernandez; Alexandre Sablayrolles; Teddy Furon; Hervé Jégou; Matthijs Douze

ICASSP 2022accepted0 citations

Watermarking Images in Self-Supervised Latent Spaces

Pierre Fernandez, Alexandre Sablayrolles, Teddy Furon, Hervé Jégou, Matthijs Douze

Abstract

We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time. Our method can operate at any resolution and creates watermarks robust to a broad range of transformations (rotations, crops, JPEG, contrast, etc). It significantly outperforms the previous zero-bit methods, and its performance on multi-bit watermarking is on par with state-of-the-art encoder-decoder architectures trained end-to-end for watermarking. The code is available at github.com/facebookresearch/ssl_watermarking.

BibTeX

@inproceedings{icassp2022_watermarkingimag,
  title = {Watermarking Images in Self-Supervised Latent Spaces},
  author = {Pierre Fernandez and Alexandre Sablayrolles and Teddy Furon and Hervé Jégou and Matthijs Douze},
  booktitle = {ICASSP 2022},
  year = {2022}
}