Algebraic Hessenberg Decomposition Method Optimized by Genetic Algorithm for Zero Watermarking Technique

Mathematics and in particular linear algebra play an important role in most scientific fields. Image processing has had the largest share of the use of linear algebra, especially image watermarking techniques. This paper aims to use the Hessenberg decomposition method (HDM) as an algebraic transform to construct a zero watermarking technique and to optimize this technique using the genetic algorithm (GA) in the YCbCr space. After several preprocesses on the Y component, the feature matrix bits are extracted from the features matrices after applying HDM to obtain the final zero-secret. The optimized zero-secret is generated by applying GA on zero-secret to optimize the zero watermarking technique. The experimental outcomes display that the technique has worked successfully and it is robust and resistant against the various attacks depending on the test of the normalized correlation (NC) values.