Learning Iterative Neural Optimizers for Image Steganography
Authors: Xiangyu Chen, Varsha Kishore, Kilian Q Weinberger
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the efficacy of LISO extensively across multiple datasets. We demonstrate that at test-time, with unseen cover images and random bit strings, the optimizer can reliably circumvent bad local minima and find a low-error solution within only a few iterative steps that already outperforms all previous encoder-decoder-based approaches. |
| Researcher Affiliation | Academia | Xiangyu Chen , Varsha Kishore & Kilian Q Weinberger Department of Computer Science Conell University Ithaca, NY 14850, USA {xc429,vk352,kqw4}@cornell.edu |
| Pseudocode | Yes | Algorithm 1 Iterative Optimization |
| Open Source Code | Yes | The code for LISO is available at https://github.com/cxy1997/LISO. |
| Open Datasets | Yes | We evaluate our method on three public datasets: 1) Div2k (Agustsson & Timofte, 2017) which is a scenic images dataset, 2) Celeb A (Liu et al., 2018) which consists of facial images of celebrities, and 3) MS COCO (Lin et al., 2014) which contains images of common household objects and scenes. |
| Dataset Splits | Yes | For Celeb A and MS COCO we use the first 1000 for validation and the following 1,000 for training. |
| Hardware Specification | Yes | The reported times were the average times on Div2k s validation set and the methods were run on a Nvidia Titan RTX GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as Python, PyTorch, TensorFlow, or other libraries. It only implies their use through the nature of the research. |
| Experiment Setup | Yes | During training, we set the number of encoder iterations T = 15, the step size η = 1, the decay γ = 0.8 and loss weights λ = µ = 1. During inference, we use a smaller step size η = 0.1 for a larger number of iterations T; we iterate until the error rate converges. |