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nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
Privacy-Preserving Machine Learning Deep Learning Graph Compilers
2019/8/21
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that en- ables data scientists to deploy models with popular frameworks such as Tens...
Neural Network Model Assessment for Side-Channel Analysis
Side-Channel Analysis Neural Networks Model Assessment
2019/6/19
Leakage assessment of cryptographic implementations with side-channel analysis relies on two important assumptions: leakage model and the number of side-channel traces. In the context of profiled side...
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference
multi-key homomorphic encryption packed ciphertext ring learning with errors
2019/5/21
Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted data. López-Alt et al. (STOC 2012) proposed a generalized notion of HE, called Multi-Key Homomorphic Encryption (M...
Experimental Evaluation of Deep Neural Network Resistance Against Fault Injection Attacks
fault attack neural network deep learning
2019/5/13
Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious con...
Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery
Side-Channel Attacks Deep Learning Machine Learning
2019/2/26
Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, resea...
XONN: XNOR-based Oblivious Deep Neural Network Inference
Privacy-Preserving Machine Learning Deep Learning Oblivious Inference
2019/2/25
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send ba...
COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA
Deep neural networks CNNs LSTMs ConvLSTMs Crop classification
2019/2/28
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
ORTHOSEG: A DEEP MULTIMODAL CONVOLUTONAL NEURAL NETWORK ARCHITECTURE FOR SEMANTIC SEGMENTATION OF ORTHOIMAGERY
Deep Learning Supervised Image Segmentation Residual Networks
2019/2/28
This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network archit...
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK
Hyperspectral classification feature extraction spectral channels deep learning
2019/2/28
In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE)...
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Side-channel Analysis Artificial Neural Networks Power
2018/5/28
Machine learning has become mainstream across industries. In this work we pose the following question: Is it possible to reverse engineer a neural network by using only side-channel information? We an...
SecureNN: Efficient and Private Neural Network Training
secure computation neural network training information-theoretic security
2018/5/15
Neural Networks (NN) provide a powerful method for machine learning training and prediction. For effective training, it is often desirable for multiple parties to combine their data -- however, doing ...
MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION
Pansharpening PCNN Image Fusion Multispectral Imaging Remote Sensing Segmentation
2018/5/14
The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentatio...
ABOVEGROUND BIOMASS ESTIMATION USING RECONSTRUCTED FEATURE OF AIRBORNE DISCRETE-RETURN LIDAR BY AUTO-ENCODER NEURAL NETWORK
Aboveground Biomass (AGB) Estimation Discrete-Return LiDAR Regression Auto-Encoder Neural Network
2018/5/14
Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision A...
A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA
Hyperspectral Data Classification Three-dimensional Convolution Deep CNN Feature Extraction
2018/5/14
Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectr...
QUALITY EVALUATION OF LAND-COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
Remote Sensing Land Cover classification Deep Learning Convolutional Neural Network Quality evaluation
2018/5/11
Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attri...