Search Results for author:
Found 7 papers, 3 papers with code
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image.
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allowing it to learn the data distribution based on the important edges in the input image.
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs).
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks
Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference or can be identified during the validation phase.
Deep Learning for EEG Seizure Detection in Preterm Infants
An AUC of 88. 3% was obtained when tested on preterm EEG as compared to 96. 6% obtained when tested on term EEG.
Bias and Fairness on Multimodal Emotion Detection Algorithms
Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups.
Bayesian Optimization for Macro Placement
Macro placement is the problem of placing memory blocks on a chip canvas.
