Search Results for author:
Found 9 papers, 1 papers with code
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks
Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence.
Detecting GAN generated Fake Images using Co-occurrence Matrices
The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images.
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled.
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods.
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning.
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e. g. a ruler).
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and the wisdom of the crowds.
Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization
We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes.
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
We present two graph-based algorithms for multiclass segmentation of high-dimensional data.
