Neural Cover Selection for Image Steganography
Authors: Karl Chahine, Hyeji Kim
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {karlchahine, hyeji.kim}@utexas.edu |
| 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. |