82 citations · 132 across the 9 of their papers we have counts for
12 papers · 1 filter
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Touqeer Ahmad, Mohsen Jafarzadeh, Akshay Raj Dhamija +4
There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-tra…
Self-Supervised Features Improve Open-World Learning
Akshay Raj Dhamija, Touqeer Ahmad, Jonathan Schwan +3
This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}. We accomplis…
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…
SpliceRadar: A Learned Method For Blind Image Forensics
Aurobrata Ghosh, Zheng Zhong, Terrance E Boult +1
Detection and localization of image manipulations like splices are gaining in importance with the easy accessibility of image editing softwares. While detection generates a verdict…
Reducing Network Agnostophobia
Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is n…