54 citations · 98 across the 23 of their papers we have counts for
9 papers · 1 filter
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features
Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Tejaswi Nanjundaswamy +3
With the growing number of malware and cyber attacks, there is a need for "orthogonal" cyber defense approaches, which are complementary to existing methods by detecting unique mal…
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar +2
Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the ide…
Seam Carving Detection and Localization using Two-Stage Deep Neural Networks
Lakshmanan Nataraj, Chandrakanth Gudavalli, Tajuddin Manhar Mohammed +2
Seam carving is a method to resize an image in a content aware fashion. However, this method can also be used to carve out objects from images. In this paper, we propose a two-step…
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Chandrakanth Gudavalli, Erik Rosten, Lakshmanan Nataraj +2
Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or in…
Minimal Rank Completions for Overlapping Blocks
Ethan N. Epperly, Nithin Govindarajan, Shivkumar Chandrasekaran
We consider the multi-objective optimization problem of choosing the bottom left block-entry of a block lower triangular matrix to minimize the ranks of all block sub-matrices. We…
Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices
Lakshmanan Nataraj, Michael Goebel, Tajuddin Manhar Mohammed +2
Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothi…
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…
Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar +2
We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-gra…