Intelligent Machine Learning in Image Authentication.

Citation:
Lamiaa M. El Bakrawy, N. I.Ghali, and A. E. Hassanien, "Intelligent Machine Learning in Image Authentication.", signal processing system, vol. 78, issue 2, pp. 223-237 , 2015.

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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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