133 citations · 263 across the 21 of their papers we have counts for
6 papers · 1 filter
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
Bedeuro Kim, Alsharif Abuadbba, Yansong Gao +4
As an essential processing step in computer vision applications, image resizing or scaling, more specifically downsampling, has to be applied before feeding a normally large image…
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
Yansong Gao, Bao Gia Doan, Zhi Zhang +5
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attacker's capability and affected st…
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation
Bedeuro Kim, Sharif Abuadbba, Hyoungshick Kim
Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email dete…
Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks
William Aiken, Hyoungshick Kim, Simon Woo
Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into embedding copyright protection for neural networks has be…
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim +5
A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively r…
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Yansong Gao, Minki Kim, Sharif Abuadbba +6
This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and…