3 citations · 4 across the 5 of their papers we have counts for
7 papers
Adversarial Detection without Model Information
Abhishek Moitra, Youngeun Kim, Priyadarshini Panda
Prior state-of-the-art adversarial detection works are classifier model dependent, i.e., they require classifier model outputs and parameters for training the detector or during ad…
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra +3
Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary s…
DetectX -- Adversarial Input Detection using Current Signatures in Memristive XBar Arrays
Abhishek Moitra, Priyadarshini Panda
Adversarial input detection has emerged as a prominent technique to harden Deep Neural Networks(DNNs) against adversarial attacks. Most prior works use neural network-based detecto…
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
With a growing need to enable intelligence in embedded devices in the Internet of Things (IoT) era, secure hardware implementation of Deep Neural Networks (DNNs) has become imperat…
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee +2
As neural networks gain widespread adoption in embedded devices, there is a need for model compression techniques to facilitate deployment in resource-constrained environments. Qua…
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
Rachel Sterneck, Abhishek Moitra, Priyadarshini Panda
Neural networks have achieved remarkable performance in computer vision, however they are vulnerable to adversarial examples. Adversarial examples are inputs that have been careful…