1 citations · 3 across the 8 of their papers we have counts for
8 papers
Automated Quantum Circuit Generation for Computing Inverse Hash Functions
Elena R. Henderson, Jessie M. Henderson, William V. Oxford +1
Several cryptographic systems depend upon the computational difficulty of reversing cryptographic hash functions. Robust hash functions transform inputs to outputs in such a way th…
Designing a Photonic Physically Unclonable Function Having Resilience to Machine Learning Attacks
Elena R. Henderson, Jessie M. Henderson, Hiva Shahoei +4
Physically unclonable functions (PUFs) are designed to act as device 'fingerprints.' Given an input challenge, the PUF circuit should produce an unpredictable response for use in s…
A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks
Jessie M. Henderson, Elena R. Henderson, Clayton A. Harper +5
Physically unclonable functions (PUFs) identify integrated circuits using nonlinearly-related challenge-response pairs (CRPs). Ideally, the relationship between challenges and corr…
Demonstration of a Hardware-Independent Toolkit for Automated Quantum Subcircuit Synthesis
Elena R. Henderson, Jessie M. Henderson, Aviraj Sinha +2
The quantum computer has become contemporary reality, with the first two-qubit machine of mere decades ago transforming into cloud-accessible devices with tens, hundreds, or -- in…
CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques
Andrew Havard, Theodore Manikas, Eric C. Larson +1
We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approach…
A Programmable True Random Number Generator Using Commercial Quantum Computers
Aviraj Sinha, Elena R. Henderson, Jessie M. Henderson +2
Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes suc…
Automatic Modulation Classification with Deep Neural Networks
Clayton Harper, Mitchell Thornton, Eric Larson
Automatic modulation classification is a desired feature in many modern software-defined radios. In recent years, a number of convolutional deep learning architectures have been pr…
Automated Quantum Memory Compilation with Improved Dynamic Range
Aviraj Sinha, Elena R. Henderson, Jessie M. Henderson +1
Emerging quantum algorithms that process data require that classical input data be represented as a quantum state. These data-processing algorithms often follow the gate model of q…