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Found 28 papers, 0 papers with code
Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists' style imitation, raising questions about ethical and security concerns.
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack
The advancements in generative AI have enabled the improvement of audio synthesis models, including text-to-speech and voice conversion.
SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning
Human Activity Recognition (HAR) is a well-studied field with research dating back to the 1980s.
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition
Various types of sensors can be used for Human Activity Recognition (HAR), and each of them has different strengths and weaknesses.
Ranked #1 on
Human Activity Recognition
on PAMAP2
(Accuracy metric)
Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Framework
We leverage a triad of deep learning models designed to distill essential representations from transaction sequencing, subgraph connectivity, and narrative generation to significantly streamline the analyst's investigative process.
Crossed-IoT device portability of Electromagnetic Side Channel Analysis: Challenges and Dataset
We present an approach to collect the EM-SCA datasets and demonstrate the feasibility of using transfer learning to obtain more meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT devices.
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture
We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture.
Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model
The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes.
Structural Textile Pattern Recognition and Processing Based on Hypergraphs
For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing.
SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation
Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe.
Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
Swift response to the detection of endangered minors is an ongoing concern for law enforcement.
OAK: Ontology-Based Knowledge Map Model for Digital Agriculture
This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited.
Predicting Soil pH by Using Nearest Fields
In precision agriculture (PA), soil sampling and testing operation is prior to planting any new crop.
Knowledge Map: Toward a New Approach Supporting the Knowledge Management in Distributed Data Mining
The integrating and cooperating of these two steps need an effective knowledge management, concretely an efficient map of knowledge in order to take the advantage of mined knowledge to guide mining the data.
Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge.
Designing and Implementing Data Warehouse for Agricultural Big Data
We also evaluate the performance of our data warehouse.
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
Our approach is to build a neural network model utilizing Adversarial Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who leverages off-the-shelf tools and system applications.
A Survey of Electromagnetic Side-Channel Attacks and Discussion on their Case-Progressing Potential for Digital Forensics
EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices.
Cryptography and Security General Literature
Performance study of distributed Apriori-like frequent itemsets mining
In this article, we focus on distributed Apriori-based frequent itemsets mining.
Digital forensic investigation of two-way radio communication equipment and services
To date, there is little research on the digital traces in modern radio communication equipment.
Cryptography and Security Computers and Society
Hierarchical Aggregation Approach for Distributed clustering of spatial datasets
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique.
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks
Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies.
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets
We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifier with superior accuracy on the minority class and overall.
Toward a new approach for massive LiDAR data processing
In this paper, we present a comparative study of software libraries and algorithms to optimise the processing of LiDAR data.
Feature Selection Parallel Technique for Remotely Sensed Imagery Classification
Remote sensing research focusing on feature selection has long attracted the attention of the remote sensing community because feature selection is a prerequisite for image processing and various applications.
Efficient Large Scale Clustering based on Data Partitioning
However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms.
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network
Most of the cur- rent research on anomaly detection is based on the learning of normally and anomaly behaviors.
An Integrated Classification Model for Financial Data Mining
Nowadays, financial data analysis is becoming increasingly important in the business market.
