7 citations · 15 across the 6 of their papers we have counts for
6 papers
CNNs Avoid Curse of Dimensionality by Learning on Patches
Vamshi C. Madala, Shivkumar Chandrasekaran, Jason Bunk
Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the g…
Adversarially Optimized Mixup for Robust Classification
Jason Bunk, Srinjoy Chattopadhyay, B. S. Manjunath +1
Mixup is a procedure for data augmentation that trains networks to make smoothly interpolated predictions between datapoints. Adversarial training is a strong form of data augmenta…
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions
Michael Goebel, Jason Bunk, Srinjoy Chattopadhyay +3
Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment. Automatic reverse engineering of the toolchains behind th…
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
Arjuna Flenner, Lawrence Peterson, Jason Bunk +3
The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images.…
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj +6
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting…
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed +6
Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling fe…