A blind spatial domain-based image watermarking using texture analysis and association rules mining

In aims to ensure images authentication, this paper proposes a blind spatial domain-based image watermarking using texture analysis and association rules mining. The idea is to identify the strongly textured locations in the host image for inserting the watermark. Indeed, texture is correlated with the Human Visual System (HVS). It can therefore be helpful in designing a watermarking approach to enhance the imperceptibility and the robustness. Here a solution is proposed in which four gray-scale histogram based-image features (DC, skewness, kurtosis, and entropy) are chosen as input data for designing association rules. Subsequently, the Apriori algorithm is applied to mine the relationships between the selected features. The higher significant relationships between the selected features are used to identify the strongly textured blocks for watermark embedding. Two strong parameters (lift and confidence) computed using association rules mining were used to design a means of blind watermarking. The experimental results show that interesting ratios of imperceptibility, robustness and embedding rate with low execution time can be obtained by this approach.