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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
A Machine Learning Approach To Deepfake Detection, Delaney Conrad
A Machine Learning Approach To Deepfake Detection, Delaney Conrad
All Undergraduate Theses and Capstone Projects
The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …
Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat
Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat
All Works
The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is …
Latent Semantic Indexing In The Discovery Of Cyber-Bullying In Online Text, Jacob L. Bigelow
Latent Semantic Indexing In The Discovery Of Cyber-Bullying In Online Text, Jacob L. Bigelow
Computer Science Summer Fellows
The rise in the use of social media and particularly the rise of adolescent use has led to a new means of bullying. Cyber-bullying has proven consequential to youth internet users causing a need for a response. In order to effectively stop this problem we need a verified method of detecting cyber-bullying in online text; we aim to find that method. For this project we look at thirteen thousand labeled posts from Formspring and create a bank of words used in the posts. First the posts are cleaned up by taking out punctuation, normalizing emoticons, and removing high and low …
Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley
Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley
Computer Science Summer Fellows
Cyberbullying is a type of bullying that uses technology such as cell phones to harass or malign another person. To detect acts of cyberbullying, we are developing an algorithm that will detect cyberbullying in SMS (text) messages. Over 80,000 text messages have been collected by software installed on cell phones carried by participants in our study. This paper describes the development of the algorithm to detect cyberbullying messages, using the cell phone data collected previously. The algorithm works by first separating the messages into conversations in an automated way. The algorithm then analyzes the conversations and scores the severity and …
Immunology Inspired Detection Of Data Theft From Autonomous Network Activity, Theodore O. Cochran
Immunology Inspired Detection Of Data Theft From Autonomous Network Activity, Theodore O. Cochran
CCE Theses and Dissertations
The threat of data theft posed by self-propagating, remotely controlled bot malware is increasing. Cyber criminals are motivated to steal sensitive data, such as user names, passwords, account numbers, and credit card numbers, because these items can be parlayed into cash. For anonymity and economy of scale, bot networks have become the cyber criminal’s weapon of choice. In 2010 a single botnet included over one million compromised host computers, and one of the largest botnets in 2011 was specifically designed to harvest financial data from its victims. Unfortunately, current intrusion detection methods are unable to effectively detect data extraction techniques …
Study Of Feasability For Phase Difference Extraction Using Software Defined Radio In Location Analysis, Paul Miller
Study Of Feasability For Phase Difference Extraction Using Software Defined Radio In Location Analysis, Paul Miller
Masters Theses
Here is a method for using the phase of an HF radio signal for use in location analysis. This computation is implemented in a software defined radio processing block in the GNU Radio environment. The signals analyzed are received by an Ettus Research USRP SDR. We created a phase analysis system called WMU Rootsync, which compares the roots of a received signal to the roots of a generated reference signal for the phase analysis. This research describes a prototype method for phase analysis only. Future projects may use these ideas differently than presented here. We intend for future projects to …