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..
DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding
Authors: Yingjie Lao, Weijie Zhao, Peng Yang, Ping Li9595-9603
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on various models over a wide range of datasets demonstrate the effectiveness and efficiency of the proposed Deep Auth. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA 2 Cognitive Computing Lab, Baidu Research, Bellevue, WA 98004, USA |
| Pseudocode | Yes | Algorithm 1: Key Sample Generation; Algorithm 2: Signature Embedding |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We leverage Le Net5 for MNIST, VGG16 for CIFAR10, and Res Net50 for Image Net. |
| Dataset Splits | Yes | We assume the trusted party has access to a held-out validation dataset for evaluating the performance. We leverage Le Net5 for MNIST, VGG16 for CIFAR10, and Res Net50 for Image Net. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | Our implementation was based on the Paddle Paddle deep learning platform. No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | We use 0.0005, 0.002, 0.02 for MNIST, CIFAR10, and Image Net, respectively. The learning rate (Lr) of the embedding process is given as 1e-4, 1e-3 for MNIST and 1e-6, 1e-5 for CIFAR10. For fine-tuning, the learning rate is 1e-4. |