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..
Flow-Based Robust Watermarking with Invertible Noise Layer for Black-Box Distortions
Authors: Han Fang, Yupeng Qiu, Kejiang Chen, Jiyi Zhang, Weiming Zhang, Ee-Chien Chang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of the proposed framework in terms of visual quality and robustness. Compared with the state-of-the-art architecture, the visual quality (measured by PSNR) of the proposed framework improves by 2d B and the extraction accuracy after JPEG compression (QF=50) improves by more than 4%. Besides, the robustness against black-box distortions can be greatly achieved with more than 95% extraction accuracy. |
| Researcher Affiliation | Academia | National University of Singapore University of Science and Technology of China |
| Pseudocode | No | The paper provides network architectures, equations, and descriptions of processes, but does not include a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | 1Source code: https://github.com/QQiuyp/FIN. |
| Open Datasets | Yes | In this paper, the DIV2K (Agustsson and Timofte 2017) training dataset is used for training. The testing dataset we choose is the classical USC-SIPI (Viterbi 1977) image dataset. |
| Dataset Splits | No | The paper states that the DIV2K training dataset is used for training and the USC-SIPI image dataset is used for testing, but it does not specify any train/validation/test splits, percentages, or methodology for splitting the datasets. |
| Hardware Specification | Yes | The framework is implemented by Py Torch (Collobert, Kavukcuoglu, and Farabet 2011) and is run on one NVIDIA RTX 3090ti. |
| Software Dependencies | No | The paper mentions 'Py Torch (Collobert, Kavukcuoglu, and Farabet 2011)' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | The parameters of λ1 and λ2 are fixed as 1 and 10, respectively. The number of invertible neural blocks in FED n is set as 8 and the number of invertible noise blocks k is set as 8. For parameter optimization of each network, we utilize Adam (Kingma and Ba 2015) with a learning rate of 1e-4 as default hyperparameters. |