Enhancement of capacity, detectability and distortion of BMP, GIF and JPEG images with distributed steganography

Araujo, Istteffanny Isloure and Kazemian, Hassan (2019) Enhancement of capacity, detectability and distortion of BMP, GIF and JPEG images with distributed steganography. International Journal of Computer Network and Information Security (IJCNIS), 11 (11). pp. 21-27. ISSN 2074-9090


The advance of Big Data and Internet growth has driven the need for more abundant storage to hold and share data. People are sending more messages to one another and paying attention to the aspects of privacy and security as opposed to previous decades. One of the types of files that are widely shared and instantaneous available over the web are images. They can become available as soon as a shot is taken and keep this closely related to the owner; it is not easy. It has been proposed here to use Steganography to embed information of the author, image description, license of usage and any other secrete information related to it. Thinking of this, an analysis of the best file types, considering capacity, detectability, and distortion was necessary to determine the best solution to tackle current algorithm weaknesses. The performance of BMP, GIF, and JPEG initialises the process of addressing current weaknesses of Steganographic algorithms. The main weaknesses are capacity, detectability and distortion to secure copyright images. Distributed Steganography technique also plays a crucial part in this experiment. It enhances all the file formats analysed. It provided better capacity and less detectability and distortion, especially with BMP. BMP has found to be the better image file format. The unique combination of Distributed Steganography and the use of the best file format approach to address the weaknesses of previous algorithms, especially increasing the capacity. It will undoubtedly be beneficial for the day to day user, social media creators and artists looking to protect their work with copyright.

IJCNIS-V11-N11-3 final.pdf - Published Version

Download (385kB) | Preview


Downloads per month over past year

Downloads each year

View Item View Item