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Intelligent Machine Learning in Image Authentication

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Abstract

Image authentication techniques have recently gained great attention due to its importance for a large number of multimedia applications. Digital images are increasingly transmitted over non-secure channels such as the Internet. Therefore, military, medical and quality control images must be protected against attempts to manipulate them; such manipulations could tamper the decisions based on these im- ages. To protect the authenticity of multimedia images, there are several approaches including conventional cryptography, fragile and semi-fragile watermarking and dig- ital signatures that are based on the image content. The aim of this paper is to present a review on different Machine learning techniques as Fuzzy Set Theory, Rough Set Theory, Rough K-means clustering, Near Sets and Nearness Approximation Spaces, Vector quantization, Genetic Algorithm, Particle Swarm Optimization, Support Vec- tor Machine and applying them in image authentication.

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  1. Al-Azhar University, Faculty of Science, Cairo, Egypt

    Lamiaa M. El Bakrawy & Neveen I. Ghali

  2. Cairo University, Faculty of Computers and Information, Cairo, Egypt

    Aboul ella Hassanien

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  1. Lamiaa M. El Bakrawy
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  2. Neveen I. Ghali
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El Bakrawy, L.M., Ghali, N.I. & ella Hassanien, A. Intelligent Machine Learning in Image Authentication. J Sign Process Syst 78, 223–237 (2015). https://doi.org/10.1007/s11265-013-0817-4

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