DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding
Authors: Yingjie Lao, Weijie Zhao, Peng Yang, Ping Li9595-9603
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |