Quantization-based Watermarking Scheme Research Articles

Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

Research on anti-counterfeiting quick response 2D barcode techniques based on digital watermark

In order to increase the watermarking capacity and improve the anti-counterfeiting performance of quick response (QR) two-dimensional (2D) barcode based on digital watermarking technique, this paper proposes a kind of graying algorithm for binary image to make transform domain watermarking methods possible. Based on the graying algorithm, the quantization-based watermarking scheme in discrete wavelet transform (DWT) domain is then designed. The experiments show that the proposed watermarking scheme for QR 2D barcode greatly improves anti-counterfeiting performance without loss of any barcode information.

A Quantization based Watermarking Scheme for Image Authentication

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Robustness Enhancement for Noncentric Quantization-Based Image Watermarking

This paper presents a novel approach for improving the robustness of the quantization-based watermarking scheme. Unlike the conventional approach, which quantizes the host value to the center of the objective interval, the proposed scheme offers the flexibility of determining the quantized value. Given tolerable embedding-induced distortion and the host value, the quantized value can be analytically determined in a closed-form solution, instead of being fixed at the center of the interval. To objectively compare the robustness, different watermarking schemes are implemented in the same distortion-to-noise ratio (DNR) scenario and then the probabilities of error detection are measured. Simulation results indicate that the proposed scheme, compared to the conventional quantization watermarking, could reduce the error probability by 0.113 when DNR is set to 0 dB, whereas the reduction is only 0.037 for the recent Wu's approach. Furthermore, without the finite possible values of the watermarked data, the proposed scheme provides sufficient nondisclosure for the quantization step, whereas in the conventional quantization-based watermarking, the quantization step can be easily inferred from the watermarked data