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20162024
most citedHybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

477 citations · 565 across the 17 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CR2021★ 1 cited

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…

cs.CR2021★ 2 cited

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…

cs.CV2021★ 6 cited

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…

cs.CV2021

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…

cs.CV2021

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…

cs.CR2021

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…