Watermarking Images in Self-Supervised Latent Spaces

• URL: http://arxiv.org/abs/2112.09581v1
• Date: Fri, 17 Dec 2021 15:52:46 GMT
• Title: Watermarking Images in Self-Supervised Latent Spaces
• Authors: Pierre Fernandez, Alexandre Sablayrolles, Teddy Furon, Herv\'e J\'egou, Matthijs Douze
• Abstract summary: 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.
• Score: 75.99287942537138
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Our implementation and models will be made publicly available.

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