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Data Hiding by Unsupervised Machine Learning Using Clustering K-mean Technique | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 4, Volume 21, Issue 4, 2021, Pages 37-49 PDF (1.3 M) | ||
| DOI: 10.33103/uot.ijccce.21.4.4 | ||
| Authors | ||
| Hiba Hamdi Hassan; Maisa'a Abid Ali Khodher | ||
| Department of Computer Science, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Steganography includes hiding text, image, or any sentient information inside another image, video, or audio. It aims to increase individuals’ use of social media, the internet and web networks to securely transmit information between sender and receiver and an attacker will not be able to detect its information. The current article deals with steganography that can be used as machine learning method, it suggests a new method to hide data by using unsupervised machine learning (clustering k-mean algorithm). This system uses hidden data into the cover image, it consists of three steps: the first step divides the cover image into three clusterings that more contrast by using k-means cluster, the selects a text or image to be converted to binary by using ASCII code, the third step hides a binary message or binary image in the cover image by using sequential LSB method. After that, the system is implemented. The objective of the suggested system is obtained, using Unsupervised Machine Learning (K-mean technique) to securely send sensitive information without worrying about man-in-the-middle attack was proposed. Such a method is characterized by high security and capacity. Through evaluation, the system uses PSNR, MSE, Entropy, and Histogram to hide the secret message and secret image in the spatial domain in the cover image. | ||
| Keywords | ||
| Steganography; (LSB); K-mean; Cluster; Machine learning | ||
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