Robust digital image watermarking based on fuzzy inference system and back propagation neural networks using DCT

This paper presents an image watermarking technique which exploits the human visual system (HVS), two artificial intelligence techniques [fuzzy inference system (FIS) and back propagation neural networks (BPNN)], and discrete cosine transform. The integration of FIS and BPNN results in a hybrid intelligence technique named neuro-fuzzy system, which combines the advantages of both the techniques. This system uses HVS model for calculating the weighing factor by giving the HVS parameters as inputs to the FIS and BPNN. The computed weighing value is used for embedding the watermark with maximum energy and imperceptibility. Compared to the existing methods, the proposed algorithm makes the watermark invisible and robust against numerous watermarking attacks including low-pass filtering, median filtering, rotation, JPEG compression, row–column copying, row–column blanking, image blurring, etc. Performance metrics used for comparison are peak signal-to-noise ratio and normalized cross correlation.