1 to 5 of 5 Results
Oct 16, 2024
Wang, Si; Liu, Wenye; Chang, Chip-Hong, 2024, "Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network", https://doi.org/10.21979/N9/8LWB8D, DR-NTU (Data), V1
This dataset contains shell scripts for configurable glitch injection through GPIO as well as the decision tree detector. It also contains partial program source code for glitch control and the corresponding generated bitstream. |
Oct 16, 2024
Wang, Si; Chang, Chip Hong, 2024, "Related Data for: Fingerprinting Deep Neural Networks - a DeepFool Approach", https://doi.org/10.21979/N9/ZDWQLI, DR-NTU (Data), V1, UNF:6:SczDG/K0h5MoF1viJWM7Pg== [fileUNF]
This dataset contains partial program source code and the data for analysis for the paper "Fingerprinting Deep Neural Networks - a DeepFool Approach" |
Feb 28, 2020
Wang, Si, 2020, "Replication Data for: A Low-power Reliability Enhanced Arbiter Physical Unclonable Function Based on Current Starved Multiplexers", https://doi.org/10.21979/N9/MWVFNO, DR-NTU (Data), V1, UNF:6:GTgHLjDPINshZKfoch7QwQ== [fileUNF]
This dataset contains partial program source code and the data for analysis for the paper "A Low-power Reliability Enhanced Arbiter Physical Unclonable Function" |
Feb 28, 2020
Wang, Si, 2020, "Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection", https://doi.org/10.21979/N9/WCIL7X, DR-NTU (Data), V1
This dataset contains the program source code, model file and experimental data for analysis of the paper "Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection" |
Feb 28, 2020
Wang, Si, 2020, "Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs", https://doi.org/10.21979/N9/YPY0EB, DR-NTU (Data), V1
This dataset contains model file, program source code and the experimental data for the analysis of the paper " Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs". |