Search Results for author:
Found 11 papers, 3 papers with code
MalGrid: Visualization Of Binary Features In Large Malware Corpora
The number of malware is constantly on the rise.
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis
Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the identity and behavior of PDF malware.
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features
In this paper, we propose a novel and orthogonal malware detection (OMD) approach to identify malware using a combination of audio descriptors, image similarity descriptors and other static/statistical features.
Seam Carving Detection and Localization using Two-Stage Deep Neural Networks
In this paper, we propose a two-step method to detect and localize seam carved images.
Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices
While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations.
Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning
Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance.
Detection, Attribution and Localization of GAN Generated Images
Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers.
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.
