Acceso al sitio web del Consejo de Políticas de Infraestructura

   
Redirigiendo al acceso original de articulo en 16 segundos...
Inicio  /  Applied Sciences  /  Vol: 10 Par: 22 (2020)  /  Artículo
ARTÍCULO
TITULO

A False Negative Study of the Steganalysis Tool Stegdetect

Benjamin Aziz    
Jeyong Jung    
Julak Lee and Yong-Tae Chun    

Resumen

In this study, we evaluated one of the modern automated steganalysis tools, Stegdetect, to study its false negative rates when analysing a bulk of images. In so doing, we used JPHide method to embed a randomly generated messages into 2000 JPEG images. The aim of this study is to help digital forensics analysts during their investigations by means of providing an idea of the false negative rates of Stegdetect. This study found that (1) the false negative rates depended largely on the tool?s sensitivity values, (2) the tool had a high false negative rate between the sensitivity values from 0.1 to 3.4 and (3) the best sensitivity value for detection of JPHide method was 6.2. It is therefore recommended that when analysing a huge bulk of images forensic analysts need to take into consideration sensitivity values to reduce the false negative rates of Stegdetect.

PÁGINAS
pp. 0 - 0

 Artículos similares

       
 
Rowa Aljondi, Salem Saeed Alghamdi, Abdulrahman Tajaldeen, Shareefah Alassiri, Monagi H. Alkinani and Thomas Bertinotti    
Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast canc... ver más
Revista: Applied Sciences

 
Nureni Ayofe Azeez and Emad Fadhal    
Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the... ver más
Revista: Applied Sciences

 
William Villegas-Ch and Jaime Govea    
This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickl... ver más

 
Norah Abanmi, Heba Kurdi and Mai Alzamel    
The prevalence of malware attacks that target IoT systems has raised an alarm and highlighted the need for efficient mechanisms to detect and defeat them. However, detecting malware is challenging, especially malware with new or unknown behaviors. The ma... ver más
Revista: Applied Sciences

 
Samuel Ndichu, Tao Ban, Takeshi Takahashi and Daisuke Inoue    
Intrusion analysis is essential for cybersecurity, but oftentimes, the overwhelming number of false alerts issued by security appliances can prove to be a considerable hurdle. Machine learning algorithms can automate a task known as security alert data a... ver más
Revista: Applied Sciences