54 citations · 63 across the 3 of their papers we have counts for
6 papers
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…
Multiple-Identity Image Attacks Against Face-based Identity Verification
Jerone T. A. Andrews, Thomas Tanay, Lewis D. Griffin
Facial verification systems are vulnerable to poisoning attacks that make use of multiple-identity images (MIIs)---face images stored in a database that resemble multiple persons,…
Batch Normalization is a Cause of Adversarial Vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay +2
Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter…
A New Angle on L2 Regularization
Thomas Tanay, Lewis D Griffin
Imagine two high-dimensional clusters and a hyperplane separating them. Consider in particular the angle between: the direction joining the two clusters' centroids and the normal t…
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations
Thomas Tanay, Jerone T. A. Andrews, Lewis D. Griffin
Designing models that are robust to small adversarial perturbations of their inputs has proven remarkably difficult. In this work we show that the reverse problem---making models m…
Adversarial Training Versus Weight Decay
Angus Galloway, Thomas Tanay, Graham W. Taylor
Performance-critical machine learning models should be robust to input perturbations not seen during training. Adversarial training is a method for improving a model's robustness t…