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Watermarking Images in Self-Supervised Latent Spaces

2021-12-17 15:52:46
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. Our implementation and models will be made publicly available.

Abstract (translated)

URL

https://arxiv.org/abs/2112.09581

PDF

https://arxiv.org/pdf/2112.09581.pdf


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