Logarithmic Spread-Transform Dither Modulation watermarking Based on Perceptual Model

Abstract

Logarithmic Quantization Index Modulation (LQIM) is an important extension of the original quantization-based watermarking method. However, it is well known that it is sensitive to valumetric scaling attack and easy to result in sign error after quantization and attacks. For that, in this paper, we propose a new method, namely Logarithmic Spread-Transform Dither Modulation Based on Perceptual Model (LSTDM-WM). In this regard the host signal is first projected onto a random vector and transformed using a novel Logarithmic Quantization function. Then the transformed signal is quantized regarding the watermark data and the watermarked signal is obtained by applying inverse transform to the quantized signal. The perceptual model is further exploited to adjust the quantization step adaptively for watermark embedding. Experimental results indicate that our proposed scheme overcomes two challenges cited above and has superior performance in comparison with conventional LQIM and former proposed schemes of STDM.

A Novel STDM Watermarking Using Visual Saliency-Based JND Model

“…The experiments were conducted to compare the performance of the proposed scheme and other proposed STDM improvements, termed as STDM-RW [7], STDM-AdpWM [12], STDM-RDMWm [13], and LSTDM-WM [34]. Meanwhile, three kinds of attacks (Gaussian noise with mean zero variance ranging from 0 to 15; JPEG compression, where the JPEG quality factor varies from 20 to 100; and volumetric scaling attacks that can reduce the image intensities as scaling factor varies from 0.1 to 1.5) were used to verify the performance of the proposed models.…”

“…The experimental results are demonstrated in Figure 7. From the robustness results, the proposed scheme outperforms STDM-RW [7], STDM-AdpWM [12], STDM-RDMWm [13], and LSTDM-WM [34] schemes. Our proposed scheme has average BER values 3% lower than the STDM-RW and STDM-RDMWm schemes.…”

Blind Image Watermarking using Normalized STDM robust against Fixed Gain Attack

“…RDM achieves a better performance against the FGA attack, with a limitation against the additive noise. A number of solutions have been proposed using perceptual models [13]- [18] based on Watson's model [19] to improve the fidelity and provide robustness to FGA attack. Watson provides a perceptual model for computing the slack associated with each DCT coefficient within an 8×8 block, and those slacks are used to select the projection vector and/or to determine the quantization step size during the embedding and decoding process.…”

Logarithmic STDM watermarking using visual saliency‐based JND model

“…In this Letter, a novel VS-based watermarking method for a monochrome image is proposed, in which the VS model in the discrete cosine transform (DCT) domain is introduced to modulate the perceptual JND model with a new numerical measure. Consequently, the proposed VS-based JND model is used to adjust the quantisation step adaptively for the logarithmic spread transform dither modulation (STDM) watermarking framework [2]. Experiments show the proposed scheme has enhanced robustness against common attacks.…”

“…VS-based JND model: In the logarithmic STDM watermarking scheme with the perceptual JND model [2], Watson's perceptual DCT-based JND model for luminance adaptation effect serve as the foundation to adjust the quantisation step adaptively. However, the existing JND model only measures the visual effect in the image with an equal attention level and the actual masking estimation would not be complete without VS.…”