1 citations · 1 across the 2 of their papers we have counts for
4 papers
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees
Jasjeet Dhaliwal, Kyle Hambrook
Generative priors have been shown to provide improved results over sparsity priors in linear inverse problems. However, current state of the art methods suffer from one or more of…
Recovery Guarantees for Compressible Signals with Adversarial Noise
Jasjeet Dhaliwal, Kyle Hambrook
We provide recovery guarantees for compressible signals that have been corrupted with noise and extend the framework introduced in \cite{bafna2018thwarting} to defend neural networ…
Utility Preserving Secure Private Data Release
Jasjeet Dhaliwal, Geoffrey So, Aleatha Parker-Wood +1
Differential privacy mechanisms that also make reconstruction of the data impossible come at a cost - a decrease in utility. In this paper, we tackle this problem by designing a pr…
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning
Jasjeet Dhaliwal, Saurabh Shintre
Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequenc…