71 citations · 141 across the 6 of their papers we have counts for
11 papers
Adversarial Attack on Deep Learning-Based Splice Localization
Andras Rozsa, Zheng Zhong, Terrance E. Boult
Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices. Recent best performing image-forensics algorithms…
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations
Andras Rozsa, Terrance E. Boult
Adversarial examples contain small perturbations that can remain imperceptible to human observers but alter the behavior of even the best performing deep learning models and yield…
Facial Attributes: Accuracy and Adversarial Robustness
Andras Rozsa, Manuel Günther, Ethan M. Rudd +1
Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract fac…
Adversarial Robustness: Softmax versus Openmax
Andras Rozsa, Manuel Günther, Terrance E. Boult
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning model…
Towards Robust Deep Neural Networks with BANG
Andras Rozsa, Manuel Gunther, Terrance E. Boult
Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of…
Are Accuracy and Robustness Correlated?
Andras Rozsa, Manuel Günther, Terrance E. Boult
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In thi…