Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Watermark Anything With Localized Messages
Authors: Tom Sander, Pierre Fernandez, Alain Oliviero Durmus, Teddy Furon, Matthijs Douze
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that WAM is competitive with state-of-the art methods in terms of imperceptibility and robustness, especially against inpainting and splicing, even on high-resolution images. |
| Researcher Affiliation | Collaboration | 1Meta FAIR 2CMAP, Ecole polytechnique 3Centre Inria de L'Universit e de Rennes |
| Pseudocode | No | Section C ALGORITHMS DETAILS describes DBSCAN (Density-Based Spatial Clustering of Applications with Noise) with numbered steps for its functionality but does not present it in a structured pseudocode or algorithm block format, rather as descriptive text. |
| Open Source Code | Yes | Training and inference code and model weights are available at github.com/facebookresearch/watermark-anything. The training and inference code, as well as trained models are available at: https://github. com/facebookresearch/watermark-anything |
| Open Datasets | Yes | We train our model on the MS-COCO training set with blurred faces (Lin et al., 2014)... Table 2b does the same for high-resolution images from the DIV2k (Timofte et al., 2018) validation set. |
| Dataset Splits | Yes | We train our model on the MS-COCO training set... averaged over the first 10k images of the COCO validation set... Table 2b does the same for high-resolution images from the DIV2k (Timofte et al., 2018) validation set. |
| Hardware Specification | Yes | We train with a batch size of 16 per GPU for 300 epochs using 8 V100 GPUs which takes roughly 2 days. |
| Software Dependencies | No | The code is based on Py Torch... The first training phase (Sec. 4.2) is optimized with Adam W (Kingma, 2014; Loshchilov, 2017)... For the extraction of multiple messages, we use the Scikit-learn DBSCAN implementation (Pedregosa et al., 2011). While PyTorch and Scikit-learn are mentioned as key components, specific version numbers for these software components are not explicitly provided in the text. |
| Experiment Setup | Yes | All experiments are run with nbits = 32... We train at resolution h w = 256 256... The first training phase (Sec. 4.2) is optimized with Adam W... with a linear warmup of the learning rate in 5 epochs from 1 10 6 to 1 10 4 and a cosine annealing to 1 10 6. We set λdec = 10, λdet = 1, α = 0.3. We train with a batch size of 16 per GPU for 300 epochs... The second training phase (Sec. 4.3) further trains the model with the JND attenuation for 200 epochs... During this phase, αJND = 2... We use τ = 0.5 to threshold the watermarked pixels, and we choose a rather strict setup with ε = 1 and minsamples = 1000 pixels. |