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..
Fixed Neural Network Steganography: Train the images, not the network
Authors: Varsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li, Kilian Q Weinberger
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Setup We evaluate FNNS on three diverse datasets a scenic image dataset Div2k (Agustsson & Timofte, 2017), a 2D object detection dataset MS-COCO (Lin et al., 2014) and a human face dataset Celeb A (Liu et al., 2015). Quantitative Comparison. We compare FNNS with Stegano GAN, the current state-of-the-art method, in Table 2. |
| Researcher Affiliation | Academia | Varsha Kishore , Xiangyu Chen , Yan Wang, Boyi Li & Kilian Weinberger Department of Computer Science Cornell University Ithaca, NY 14850, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Adversarial Attack for Message hiding 1: Inputs: decoder network F, cover image X, secret message M 2: Hyper-parameters: learning rate α > 0, perturbation bound ϵ > 0, optimization steps n > 0, max L-BFGS iterations k > 0 3: X X 4: for n iterations do 5: X = LBFGS(F( X), M, LBCE, k) Take k steps to optimize LBCE(F( X), M). 6: δ clip ϵ ϵ{ X X} Clip pixel value changes exceeding ϵ. 7: X clip1 0{X + δ} Clip pixel values to [0, 1]. 8: return X |
| Open Source Code | Yes | Our code is available at https://github.com/varshakishore/FNNS. |
| Open Datasets | Yes | Experimental Setup We evaluate FNNS on three diverse datasets a scenic image dataset Div2k (Agustsson & Timofte, 2017), a 2D object detection dataset MS-COCO (Lin et al., 2014) and a human face dataset Celeb A (Liu et al., 2015). |
| Dataset Splits | Yes | For each dataset, we use the provided test/validation images (if unavailable, we use the first 100 images in the dataset for validation). |
| Hardware Specification | Yes | Table 8 shows the amount of time required to encode a message with different FNNS variants and different bit rates (with standard deviations in parentheses) on a NVIDIA GTX 1080 GPU. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The hyper-parameters used for Algorithm 1 are as follows: perturbation bound ϵ = 0.3, optimization steps n = 100, and L-BFGS iterations k = 10 with early stopping if the output has zero error. In cases where the image quality of X is poor, we restart optimization with a different learning rate α. Concretely, we set the learning rate to 0.1 and change it to 0.05 or 0.5 if the output image gets a PSNR lower than 20. We train Stegano GAN models for only one epoch for FNNS-D and FNNS-DE, as we observe that a fully-trained (32 epochs) Stegano GAN decoder over-fits to its training objective such that it s hard to use it for FNNS. |