This paper presents a modified digital image watermarking method based on nonnegative matrix factorization. Firstly, host image is factorized to the product of three nonnegative matrices. Then, the centric matrix is transferred to discrete cosine transform domain. Watermark is embedded in low frequency band of this matrix and next, the reverse of the transform is computed. Finally, watermarked ...

This paper presents a modified digital image watermarking method based on nonnegative matrix factorization. Firstly, host image is factorized to the product of three nonnegative matrices. Then, the centric matrix is transferred to discrete cosine transform domain. Watermark is embedded in low frequency band of this matrix and next, the reverse of the transform is computed. Finally, watermarked ...

this paper presents a modified digital image watermarking method based on nonnegative matrix factorization. firstly, host image is factorized to the product of three nonnegative matrices. then, the centric matrix is transferred to discrete cosine transform domain. watermark is embedded in low frequency band of this matrix and next, the reverse of the transform is computed. finally, watermarked ...

nonnegative matrix factorization (nmf) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. methods in alternating least square (als) approach usually used to solve this non-convex minimization problem.  at each step of als algorithms two convex least square problems should be solved, which causes high com...

We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, attacker adds arbitrary bounded norm to given data matrix. design efficient algorithms inspired by adversarial training optimize for dictionary and coefficient matrices with enhanced generalization abilities. Extensive simulations on synt...

We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns first NMF factor are equal to input matrix, while second sparse. call this sparse separable (SSNMF), which we prove be NP-complete, as opposed can solved in polynomial time. The main motivation consider model is handle underdetermined blind ...

Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation, and hyperspectral unmixing. Given MM rank rr, NMF looks WW rr columns HH rows that M ≈ WHM≈WH. NP-hard to solve in general. However, it can be computed efficiently under the separability assumption which requires basis v...