Search Results for author:
Found 18 papers, 4 papers with code
CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data
Content creation and image editing can benefit from flexible user controls.
MalGrid: Visualization Of Binary Features In Large Malware Corpora
The number of malware is constantly on the rise.
CNNs Avoid Curse of Dimensionality by Learning on Patches
A priori theories explaining the generalization performances of deep neural networks have mostly ignored the convolutionality aspect and do not specify why CNNs are able to seemingly overcome curse of dimensionality on computer vision tasks like image classification where the image dimensions are in thousands.
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.
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.
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.
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Seam carving is a popular technique for content aware image retargeting.
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.
Adversarially Optimized Mixup for Robust Classification
Mixup is a procedure for data augmentation that trains networks to make smoothly interpolated predictions between datapoints.
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions
Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment.
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.
Predicting Generalization in Deep Learning via Local Measures of Distortion
We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory.
Exploiting Context for Robustness to Label Noise in Active Learning
Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available.
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.
Deep Algorithms: designs for networks
A new design methodology for neural networks that is guided by traditional algorithm design is presented.
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.
