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Found 6 papers, 1 papers with code
Cryptocurrency wallets: assessment and security
Digital wallet as a software program or a digital device allows users to conduct various transactions.
Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks
In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1. 5-Class (SPRITZ-1. 5C) classifier and constructed using a final dense classifier, one 2C classifier (i. e., CNNs), and two parallel 1C classifiers (i. e., auto-encoders).
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization
Our novel approach consists of changing the training strategy in the target network classifier and selecting random feature samples.
Real or Virtual: A Video Conferencing Background Manipulation-Detection System
Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings.
Detecting High-Quality GAN-Generated Face Images using Neural Networks
On the other hand, the recent development of GAN models may create high-quality face images without evidence of spatial artifacts.
Demystifying the Transferability of Adversarial Attacks in Computer Networks
In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability.
