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Color image watermarking based on reflectance component modification and guided image filtering

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Abstract

Digital images available on the Internet can be effortlessly copied and redistributed. Many image watermarking methods have been developed and are used as technical solutions to trace the ownership. Various watermark embedding and extraction techniques were considered and used in order to obtain reliable extraction and robustness against attacks. This paper presents a new color image watermarking method based on modification of the reflectance component in the Hue-Saturation-Value (HSV) color space. In the watermark embedding process, the reflectance component extracted from the S component is modified in accordance with Just Noticeable Difference (JND) thresholds derived from the V component. Guided image filtering is used in watermark extraction to predict the original reflectance component, and blind extraction is achieved by subtracting the predicted component from the watermarked one. The performances of five watermarking methods, including the proposed method, were evaluated and compared for accuracy and robustness at the equivalent quality of watermarked images. The results demonstrate that the proposed method provides an improved quality of extracted watermark by both objective and subjective quality measures. It is also more robust than the previous methods against various types of image processing based attacks, including the Stirmark benchmark.

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Acknowledgments

This research was financially supported by scholarship no. (70292/2557) from the Prince of Songkla University. We would like to thank Miss Thitiporn Pramoun and Miss Khirittha Thongkor for their fruitful discussions. The first author would like to thank Master Dhan and Mr. Teerasak Chotikawanid for their support, and Assoc. Prof. Dr. Seppo Karrila for proofreading this manuscript.

List of symbols and acronyms

HVS Human visual system

JND Just noticeable difference

q-LFD q-logarithm frequency domain

QIM Quantization index modulation

R, G, B Red, green, blue color components

H, S, V Hue, saturation, values components

i and j Pixel coordinates

Iin Intensity of an image

Iin _ L Illumination component of Iin

Iin _ R Reflectance component of Iin

ln( ) Natural logarithm

G Guidance image

II Input image

IO Output image

WG Filter kernel of Guided image filter

ωk Window ω with pixel k at the center

ak and bk Constant coefficients

|ω| Number of pixels

r Radius of a square window of guided image filter

d Radius of a square window of guidance image

\( \overline{G_k} \) and \( \overline{{I_I}_k} \) Average values of G and II in ωk

\( {\sigma}_{G_k}^2 \) Variance of G in ωk

\( {\mathcal{L}}_a \) Luminance adaptation

Ms  Spatial masking effects

\( \mathcal{B} \) Background luminance

Mp Pattern masking effect

Mc Contrast masking effect

Cl Luminance contrast

Cp Pattern complexity

I Color host image

I′ Watermarked image

SL Illumination component of S

SR Reflectance component of S

SR′ Embedded SR

\( \overline{S_R^{\prime }} \) Averaging of SR′

SR ′ ′ Predicted SR

\( {\mathcal{L}}_{a\_V} \) Luminance adaptation derived from V

Ms _ V Spatial masking effects derived from V

JNDv JND derived from V

Iw Binary watermark image

Iw′ Extracted Iw

w Watermark component

w′ Extracted w

\( {W}_{G\_\overline{S_R\prime }} \) Guided image filter kernel derived from \( \overline{S_R^{\prime }} \)

wPSNR Weighted peak signal-to-noise ratio

NVF Noise visibility function

NORM Normalization function

NC Normalized correlation

MSE Mean Squared Error

\( {\sigma}_{block}^2 \) Variance of 8×8 pixels block

T Threshold from the statistical false alarm probability

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Authors and Affiliations

  1. Multimedia Communication Laboratory, Computer Engineering Department, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Piyanart Chotikawanid & Thumrongrat Amornraksa

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  1. Piyanart Chotikawanid
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  2. Thumrongrat Amornraksa
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Correspondence to Thumrongrat Amornraksa.

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Chotikawanid, P., Amornraksa, T. Color image watermarking based on reflectance component modification and guided image filtering. Multimed Tools Appl 80, 27615–27648 (2021). https://doi.org/10.1007/s11042-021-10756-9

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