Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Iterative Neural Optimizers for Image Steganography
Authors: Xiangyu Chen, Varsha Kishore, Kilian Q Weinberger
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,306 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,946 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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 ฮท = 1, the decay ฮณ = 0.8 and loss weights ฮป = ยต = 1. During inference, we use a smaller step size ฮท = 0.1 for a larger number of iterations T; we iterate until the error rate converges. |