Search Results for author:
Found 5 papers, 2 papers with code
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection
This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme.
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.
A4 : Evading Learning-based Adblockers
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful.
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.
