Spread Transform Dither Modulation Research Articles

Robust and blind watermarking algorithm for chaotic system encryption based on discrete cosine transform-singular value decomposition

Through comparison with most frequency-domain digital watermarking approaches, the spread transform dither modulation (STDM) method exhibits a good embedding effect and is capable of meeting the requirements of blind extraction. However, the rotating attack is difficult to counter, and the cropping attack is seriously impacted by location factors. In addition, there exist numerous algorithms concentrate solely on the watermarking process, ignoring the image’s security. In these views, our work proposes a two-dimensional hyperchaotic system, the so-named Chebyshev ICMIC cascade map with the Knuth–Durstenfeld method, aiming to enhance the security of digital watermarking systems. The scrambled pixels improve the anticropping attack effect through breaking the location correlation of watermark information. For the purpose of magnifying resistance to rotation attack impact, the radial harmonic Fourier moments subtraction coefficient matrix distribution expectation is appropriately adapted to carry out the rotation correction. The performed experiments reveal that the suggested improved STDM watermarking algorithm demonstrates better robustness and invisibility, resisting more attacks than the existing methodologies. Finally, the feasibility of the proposed methods is proved based on the simulation results.

Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

Beyond-5G (B5G) aims to meet the growing demands of mobile traffic and expand the communication space. Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data, this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G. The multi-watermarking process employs spread transform dither modulation. During the watermarking procedure, Gram–Schmidt orthogonalization is used to obtain the multiple spreading vectors. Consequently, multiple watermarks can be simultaneously embedded into the same position of a multimedia signal. Moreover, the multiple watermarks can be extracted without affecting one another during the extraction process. We analyze the effect of the size of the spreading vector on the unit maximum capacity, and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity. A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

Spread-Transform Dither Modulation Watermarking of Deep Neural Network

DNN watermarking is receiving an increasing attention as a suitable mean to protect the Intellectual Property Rights associated to DNN models. Several methods proposed so far are inspired to the popular Spread Spectrum (SS) paradigm according to which the watermark bits are embedded into the projection of the weights of the DNN model onto a pseudorandom sequence. In this paper, we propose a new DNN watermarking algorithm that leverages on the watermarking with side information paradigm to decrease the obtrusiveness of the watermark and increase its payload. In particular, the new scheme exploits the main ideas of ST-DM (Spread Transform Dither Modulation) watermarking to improve the performance of a recently proposed algorithm based on conventional SS. The experiments we carried out by applying the proposed scheme to watermark different models, demonstrate its capability to provide a higher payload with a lower impact on network accuracy than a baseline method based on conventional SS, while retaining a satisfactory level of robustness.

Adaptive STDM-Based PDF Documents Watermarking Algorithm Robust to Fixed Gain Attack

Spread Transform Dither Modulation (STDM) has good performance in robustness against Additive White Gaussian Noise (AWGN) and re-quantization, so it has been widely used in image watermarking algorithms. However, STDM cannot resist Fixed Gain Attack (FGA). In the paper, we introduce an adaptive function into the traditional STDM algorithm, so that it can automatically adjust the amount of distortion according to the text line spacing in the document. Then we give an improved adaptive function to make our algorithm robust to FGA. Analysis and experimental results show that the algorithm proposed in this paper has good invisibility and high robustness against AWGN and FGA.

Using Deep learning for image watermarking attack

Digital image watermarking has justified its suitability for copyright protection and copy control of digital images. In the past years, various watermarking schemes were proposed to enhance the fidelity and the robustness of watermarked images against different types of attacks such as additive noise, filtering, and geometric attacks. It is highly important to guarantee a sufficient level of robustness of watermarked images against such type of attacks. Recently, Deep learning and neural networks achieved noticeable development and improvement, especially in image processing, segmentation, and classification. Therefore, in this paper, we studied the effect of a Fully Convolutional Neural Network (FCNN), as a denoising attack, on watermarked images. This deep architecture improves the training process and denoising performance, through which the encoder–decoder remove the noise while preserving the detailed structure of the image. FCNNDA outperforms the other types of attacks because it destroys the watermarks while preserving a good quality of the attacked images. Spread Transform Dither Modulation (STDM) and Spread Spectrum (SS) are used as watermarking schemes to embed the watermarks in the images using several scenarios. This evaluation shows that such type of denoising attack preserves the image quality while breaking the robustness of all evaluated watermarked schemes. It could also be considered a deleterious attack.

Normalized blind STDM watermarking scheme for images and PDF documents robust against fixed gain attack

Spread Transform Dither Modulation (STDM), a special case of Quantization Index Modulation (QIM), has been widely used in digital watermarking. STDM has good performance in robustness against re-quantization and random noise attacks, but it is largely vulnerable to the Fixed Gain Attack (FGA). In addition to digital images and videos watermarking applications, copyright protection for digital text such as Portable Document Format (PDF) has received particular attention and interest. In this paper, we modify the STDM watermarking scheme by making the quantization step dependent on the original samples during the embedding process and on the watermarked samples during the decoding process to resist the FGA attack and enhance the robustness against the Additive White Gaussian Noise (AWGN) attack and JPEG compression attack in both the spatial domain and frequency domain regardless of the source of elements used as cover work. Experimentations have been conducted distinctly on digital images and text PDF documents. The tested images were watermarked with a uniform fidelity, where SSIM is fixed to 0.982 and 0.953. Our approach achieves significant robustness against the FGA attack with an improvement of 98% in terms of Bit Error Rates (BER) compared to traditional STDM. As for the AWGN attack, an improvement of 21% is shown. The proposed method also presents robustness against a variety of filtering and geometric attacks, while preserving a high level of transparency.

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.

Hybrid JND model-guided watermarking method for screen content images

With the prevalence of digital products like cellphone, tablet and personal computer, the screen content images (SCIs) consisting of text, graphic, and natural scene picture becomes a significant media in various communication scenarios. In this paper, we propose a novel Spread Transform Dither Modulation (STDM) watermarking scheme based on Hybrid just noticeable distortion model for screen content images. Firstly, the original image was transformed from RGB into YCrCb to ensure stability robustness and invisibility. Then, we proposed a novel automatic classification method based on AC coefficients feature. Different from pictorial block in screen content images, structural-based contrast masking effects was incorporated to adjust the just noticeable distortion value for textual blocks. Finally, the reference image from the SIQAD image database was used to evaluate the performance of our proposed scheme. Experiments showed that our method has a good performance in term of robustness with better visual quality.

A Novel STDM Watermarking Using Visual Saliency-Based JND Model

The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

Blind digital watermarking in PDF documents using Spread Transform Dither Modulation

In this paper, a blind digital watermarking scheme for Portable Document Format (PDF) documents is proposed. The proposed method is based on a variant Quantization Index Modulation (QIM) method called Spread Transform Dither Modulation (STDM). Each bit of the secret message is embedded into a group of characters, more specifically in their x-coordinate values. The method exhibits experiments of two opposite objectives: transparency and robustness, and is motivated to present an acceptable distortion value that shows sufficient robustness under high density noises attacks while preserving sufficient transparency.

Logarithmic STDM watermarking using visual saliency‐based JND model

Owing to the validity of measuring the visual visibility for image distortion, just noticeable distortion (JND) has been widely used in the quantisation-based watermarking framework. However, the existing JND model treats every region in the image with an equal attention level. Visual saliency, which reflects the visual attention, is proposed to improve the perceptual JND model. Based on the improved model, a logarithmic spread transform dither modulation (STDM) watermarking scheme is proposed. Simulations show that the proposed watermarking scheme with the improved JND model has superior robustness compared with existing STDM schemes.

Improved spread transform dither modulation based on robust perceptual just noticeable distortion model

In the quantization-based watermarking framework, the perceptual just noticeable distortion (JND) model has been widely used to determine the quantization step size, as it can provide a better tradeoff between fidelity and robustness. However, the calculated JND values can vary due to changes introduced by watermark embedding. As a result, the mismatch problem will lead to watermark extraction errors in the absence of attacks. We present an improved spread transform dither modulation (STDM) watermarking scheme. Performance improvement with respect to the existing algorithm is obtained by a discrete cosine transform (DCT)-based perceptual JND model that is highly compatible with the STDM watermarking algorithm. The proposed scheme not only incorporates various masking effects of human visual perception, but also avoids the mismatch problem by utilizing a new measurement of the pixel intensity and edge strength. In contrast to conventional JND models, the proposed model can be theoretically invariant to the changes in the watermark-embedding processing, therefore, more fit for quantization-based watermarking. Experimental results confirm the improved robustness performance of the JND model in the STDM watermarking framework. Simulation results show that the proposed scheme is more robust than the existing JND model-based watermarking algorithms with uniform fidelity. Furthermore, our proposed scheme has a superior performance compared with previously proposed perceptual STDM schemes.

Video Watermarking Using Spatio-Temporal Masking For ST-DM

Traditional Spread Transform Dither Modulation (ST-DM) is based on the fixed quantization step size, which does not exploit the features of the video contents. Spatio-temporal masking function represents the visual threshold under which human eyes are usually not sensitive. In this paper, we adopts not only the spatial contrast sensitivity function (CSF), the luminance masking function, the contrast masking function but also the spatio-temporal masking function to account for the model of human eyes to the video. we propose using spatio-temporal perceptual model to adaptively select the quantization step size based on the calculated masking function. Experimental results show that the proposed scheme guarantees high peak signal to noise ratio (PSNR). Furthermore, compared with the scheme with fixed step size, the proposed scheme is more robust against temporal frame averaging (TFA) attack.

Step-projection-based spread transform dither modulation

Quantisation index modulation (QIM) is an important class of watermarking methods, which has been widely used in blind watermarking applications. It is well known that spread transform dither modulation (STDM), as an extension of QIM, has good performance in robustness against random noise and re-quantisation. However, the quantisation step-sizes used in STDM are random numbers not taking features of the image into account. The authors present a step projection-based approach to incorporate the perceptual model with STDM framework. Four implementations of the proposed algorithm are further presented according to different modified versions of the perceptual model. Experimental results indicate that the step projection-based approach can incorporate the perceptual model with STDM framework in a better way, thereby providing a significant improvement in image fidelity. Compared with the former proposed modified schemes of STDM, the author's best performed implementation provides powerful resistance against common attacks, especially in robustness against Gauss noise, salt and pepper noise and JPEG compression.

Adaptive Spread-Transform Dither Modulation Using a New Perceptual Model for Color Image Watermarking

Major challenges of the conventional spread-transform dither modulation (STDM) watermarking approach are two-fold: (i) it exploits a fixed watermarking strength (more particularly, the quantization index step size) to the whole cover image; and (ii) it is fairly vulnerable to the amplitude changes. To tackle the above challenges, an adaptive spread-transform dither modulation (ASTDM) approach is proposed in this paper for conducting robust color image watermarking by incorporating a new perceptual model into the conventional STDM framework. The proposed approach exploits a new perceptual model to adjust the quantization index step sizes according to the local perceptual characteristics of a cover image. Furthermore, in contrast to the conventional Watson's model is vulnerable to the amplitude changes, our proposed new perceptual model makes the luminance masking thresholds be consistent with any amplitude change, while keeping the consistence to the properties of the human visual system. In addition, certain color artifacts could be incurred during the watermark embedding procedure, since some intensity values are perceptibly changed to label the watermark. For that, a color artifact suppression algorithm is proposed by mathematically deriving an upper bound for the intensity values according to the inherent relationship between the saturation and the intensity components. Extensive experiments are conducted using 500 images selected from Corel database to demonstrate the superior performance of the proposed ASTDM approach.

Robust watermarking motion data with DL-STDM

In this paper, we propose a robust watermarking scheme called Double-Layer Spread-Transform Dither Modulation (DL-STDM) for watermarking motion data. We embed watermarks into the DCT domain of the quaternion logarithm image (QLI) representation of motion data. Experimental results demonstrate that our scheme is more robust than existing motion watermarking schemes against a wide variety of attacks, including noise addition, smoothing, cropping, attenuation, simplification, time warping, non-uniform scaling along amplitude axis and 2nd watermarking. Our scheme is useful for copyright protection and ownership identification of motion data.

Performance Analysis of ST-DM Watermarking in Presence of Nonadditive Attacks

The performance of spread-transform dither modulation (ST-DM) watermarking in the presence of two important classes of non additive attacks, such as the gain attack plus noise addition, and the quantization attack are evaluated. The analysis is developed under the assumption that the host features are independent and identically distributed Gaussian random variables, and that a minimum distance criterion is used to decode the hidden information. The theoretical bit-error probabilities are derived in closed form, thus permitting to evaluate the impact of the considered attacks on the watermark at a theoretical level. The analysis is validated by means of extensive Monte Carlo simulations. In addition to the validation of the theoretical analysis, Monte Carlo simulations permitted to abandon the hypothesis of normally distributed host features, in favor of more realistic models adopting a Laplacian or a generalized Gaussian probability density function. The general result of our analysis is that the excellent performance of ST-DM are confirmed in all cases with the only noticeable exception of the gain attack.