Ensuring Data Security in Images through Improved SVM Classifier

Wafaa Ali, Walaa Alajali, Abdulrahman D. Alhusaynat

Abstract: In the era of information and communication technology, ensuring image security has become a priority and a concern to confront cyber threats, unauthorized access and tampering. Traditional techniques provide a certain level of security but in fact lack the ability to process image anomalies, hence the challenge to propose a machine learning technique and improve the Support Vector Machine (SVM) classifier. This study presented a classifier to enhance data security in images by working with an encryption and feature extraction system that relies on higher chaotic weights for specific sections of the image. The proposed method reduces the dimensions of the image to sections and from there to the real dimensions of the image. The improved classifier achieved higher accuracy in terms of creating complex randomness in the two main stages of confusion and diffusion. The experimental results demonstrate the effectiveness of the classifier in terms of entropy = 8 and is an effective value, histogram uniformity, anomaly detection and encryption complexity. These results provide a reliable and scalable solution in many fields such as healthcare, economics and information transmission in social media. A comprehensive approach can be provided by integrating the proposed method with other methods to protect image data.

Keywords: Support Vector Machine, Image, Encryption