1 to 10 of 1,187 Results
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Unknown - 17.2 MB -
MD5: a131ba782493e06d6cf07cfad36d904a
Xilinx bitstreams with circuitries required for glitch injection |
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 84 B -
MD5: 1500915f054be3430daf49aaab821d94
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Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 84 B -
MD5: c655752909506256429c5eea267ef396
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Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 154 B -
MD5: 8625f7fea6cfa962db7be333d00f6bad
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Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 123 B -
MD5: 0d542e85b5042c2fb3b566fd2ade4e99
|
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Unknown - 25.1 MB -
MD5: 47335517383e8de403c60a8f1b9aa6b2
model file |
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 14.8 KB -
MD5: 2c71960887ccff0833259c285466edcc
classical decision tree detector implementation for resnet50 |
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 492 B -
MD5: c8e04f6db5423eb36d7107551916673f
resnet50 model implementation without glitch injection |
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Shell Script - 7.9 KB -
MD5: 4ead428dd94fa887182c844526857950
lightgbm detector implementation for resnet50 |
Oct 16, 2024 -
Related Data for: A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network
Unknown - 6.9 KB -
MD5: 8200744e79435ad9d6246844c40e0230
The timing information for each layer of computation in the PE array for ResNet50 extracted by cycle-accurate hardware profiling |
