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Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features

Dr. Sushma Jaiswal, Mr. Manoj Kumar Pandey, , ,
CSIT Department, Guru Ghasidas Central University, Bilaspur, CG

A blind color image watermarking using deep artificial neural network (DANN) in YCbCr color model has been proposed aiming at achieving fair trade-off between imperceptibility and robustness. In the proposed watermarking a random generated watermark of length 512 bit is used for the training purpose and original watermark of length 512 bits is used for the testing. Principal component analysis (PCA) is applied to select the best 10 features out of 18 statistical features. Binary classification is used for watermark extraction. It shows the average imperceptibility of 33.34 dB and average SSIM of 0.9860 for four images Lena, Peppers, Mandril and Jet. It performs well in terms of balancing the imperceptibility and robustness, for the threshold value 32. The proposed scheme takes 7.56 seconds for watermark embedding and extraction. It also shows good robustness against common image attacks including the combination of image attacks except the gaussian noise with intensity 0.06 and cropping 20% attacks. The experiment result shows that the proposed watermarking technique performs well against other technique.

Dr. Sushma Jaiswal,Mr. Manoj Kumar Pandey."Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.10, Issue 03,pp.90-98, March - 2023, URL :,

Keywords : Blind, Color Image, DANN, Robust, Watermarking, YCbCr

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