Datasets for training deep neural networks to defend software applications
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Updated
Jun 4, 2018 - Python
Datasets for training deep neural networks to defend software applications
Train AI (Keras + Tensorflow) to defend apps with Django REST Framework + Celery + Swagger + JWT - deploys to Kubernetes and OpenShift Container Platform
Network exploit detection using highly accurate pre-trained deep neural networks with Celery + Keras + Tensorflow + Redis
dga domain detected by lstm model
pytorch implementation of Parametric Noise Injection for adversarial defense
AntiNex python client for training and using pre-trained deep neural networks with JWT authentication
Manage and use pre-trained deep neural networks with a common interface for build, compile, fit, evaluate, kfold, cross validate, and predict lifecycle phases using Keras and Tensorflow
The implementation of our paper 'Visual Privacy Protection via Mapping Distortion', accepted by the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021.
The Golang library for Modzy Machine Learning Operations (MLOps) Platform
The official Java library for the Modzy Machine Learning Operations (MLOps) Platform
Code for "Adversarial attack by dropping information." (ICCV 2021)
Uncertainty guided Federated Learning
Official Implementation of IEEE TIFS paper Odyssey: Creation, Analysis and Detection of Trojan Models
Neural networks, but malefic! 😈
MSc Dissertation: Ensemble neural network for static malware classification using multiple representations
Neural networks, but malefic! 😈
The official JavaScript SDK for the Modzy Machine Learning Operations (MLOps) Platform.
Official Implementation of ICLR 2022 paper, ``Adversarial Unlearning of Backdoors via Implicit Hypergradient''
The official implementation of USENIX Security'23 paper "Meta-Sift" -- Ten minutes or less to find a 1000-size or larger clean subset on poisoned dataset.
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