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 [1].
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. |