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Found 18 papers, 6 papers with code
Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees
We propose an innovative alarm system that detects the presence of poisoned labels and a defense mechanism designed to uncover the original class labels with the aim of mitigating backdoor attacks on ensemble tree classifiers.
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data.
Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks
Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields.
Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents
To highlight the problems involved with the evaluation of the dataset's IRIS images, we conducted a large number of analyses employing Siamese Neural Networks to assess the similarities between genuine and synthetic human IRISes, such as ResNet50, Xception, VGG16, and MobileNet-v2.
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.
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework
In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection.
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.
Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System
On the other hand, users maybe want to fool people in the meeting by considering the virtual background to conceal where they are.
VIPPrint: A Large Scale Dataset of Printed and Scanned Images for Synthetic Face Images Detection and Source Linking
Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images.
A Survey of Machine Learning Techniques in Adversarial Image Forensics
Image forensic plays a crucial role in both criminal investigations (e. g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e. g., defamation).
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images.
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples
We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features.
On the Transferability of Adversarial Examples Against CNN-Based Image Forensics
In this paper, we investigate if attack transferability also holds in image forensics applications.
Cryptography and Security
CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing
Detection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task.
