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..
Neural Cover Selection for Image Steganography
Authors: Karl Chahine, Hyeji Kim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our methodology through comprehensive experimentation on public datasets such as Celeb A-HQ, Image Net, and AFHQ. Our results demonstrate that the error rates of the optimized images are an order of magnitude lower than those of the original images under specific conditions. |
| Researcher Affiliation | Academia | Karl Chahine & Hyeji Kim Department of Electrical and Computer Engineering University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative Optimization |
| Open Source Code | Yes | Our code can be found at https://github.com/karlchahine/Neural-Cover-Selection-for Image-Steganography. |
| Open Datasets | Yes | We validate our methodology through comprehensive experimentation on public datasets such as Celeb A-HQ (Karras et al. [2017]), Image Net (Russakovsky et al. [2015]), and AFHQ (Choi et al. [2020]). |
| Dataset Splits | No | The paper mentions '1000 training images from each class' and implicitly refers to test evaluation, but it does not explicitly detail a separate validation set split (e.g., percentages, counts, or a cross-validation setup) for model tuning or performance assessment. |
| Hardware Specification | Yes | All experiments were conducted using a NVIDIA A-100 GPU. |
| Software Dependencies | No | The paper mentions using specific models like 'Big GAN' and 'LISO encoder-decoder pairs' from cited works, but it does not specify software versions for programming languages, libraries, or frameworks (e.g., Python, PyTorch, CUDA versions) used in the implementation. |
| Experiment Setup | Yes | To optimize the latent vector z, we minimize the binary cross-entropy loss BCE(m, ˆm) using the Adam optimizer with a learning rate of 0.01 over 100 epochs. We configure our model with the following parameters: E = 50 epochs, T = 40 time steps, and N = 6 iterations per epoch. For optimization, we employ the Adam optimizer with a learning rate of 2E 06. |