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..
Generating steganographic images via adversarial training
Authors: Jamie Hayes, George Danezis
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our scheme on two independent image datasets, showing our novel method of studying steganographic problems is surprisingly competitive against established steganographic techniques. As a proof of concept, we implemented our adversarial training scheme on two image datasets: celebrity faces in the wild (celeb A) [14] and a standard steganography research dataset, BOSS. |
| Researcher Affiliation | Academia | Jamie Hayes University College London EMAIL George Danezis University College London The Alan Turing Institute EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. The paper describes the models' architectures in prose. |
| Open Source Code | No | No explicit statement about releasing code or a direct link to a code repository for the methodology was found. |
| Open Datasets | Yes | As a proof of concept, we implemented our adversarial training scheme on two image datasets: celebrity faces in the wild (celeb A) [14] and a standard steganography research dataset, BOSS2. 2http://agents.fel.cvut.cz/boss/index.php?mode=VIEW&tmpl=materials |
| Dataset Splits | No | For both the BOSS and Celeb A datasets, we use 10, 000 samples and split in half, creating a training set and a test set. Alice was then trained on the 5000 samples from the training set. No explicit validation set is mentioned for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | All experiments in this section were performed in Tensor Flow [1, 3], on a workstation with a Tesla K40 GPU card. |
| Software Dependencies | No | The paper mentions 'Tensor Flow [1, 3]' but does not provide specific version numbers for TensorFlow or any other software libraries used. |
| Experiment Setup | Yes | We train in batches of 32, and use the Adam optimizer [11] with a learning rate of 2 × 10−4. At each batch we alternate training either Alice and Bob, or Eve. For each experiment, we performed grid search to find the optimum loss weights, λA, λB, λE, for Alice. |