Search Results for author:
Found 10 papers, 3 papers with code
DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation
Instead of the staged pipeline, the DHBE treats the backdoor erasing task as a unified adversarial procedure, which seeks equilibrium between two different competing processes: distillation and backdoor regularization.
New Adversarial Image Detection Based on Sentiment Analysis
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e. g., DeepFool, are on the rise and outrunning adversarial example detection techniques.
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.
Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models
In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective.
Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging
A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles.
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors
We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search.
Automatically Lock Your Neural Networks When You're Away
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.
Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
Joint learning of interpretation and distillation
The extra trust brought by the model interpretation has made it an indispensable part of machine learning systems.
Detecting community structure using label propagation with weighted coherent neighborhood propinquity
In label update, a node adopts the label that has the maximum weighted-CNP instead of the one that is shared by most of its neighbors.
