Dither Modulation Research Articles

Adaptive Video Dual Domain Watermarking Scheme Based on PHT Moment and Optimized Spread Transform Dither Modulation

Adaptive Video Dual Domain Watermarking Scheme Based on PHT Moment and Optimized Spread Transform Dither Modulation

Robust Steganography for High Quality Images

With the prosperity of online social networks (OSNs), people usually share photos taken with their mobile phones to OSN, which is suitable for covert communication. However, these images have high quality factors (QFs) and will be JPEG recompressed with low QFs by OSNs, which precludes existing robust steganography methods using low QF images. Therefore, we propose a Postprocessing and precise dither Modulation based robust Adaptive Steganography method (PMAS) for high quality images. Precise dither modulation ensures the robustness of the modified coefficients and improves security by reducing the magnitude of modifications. Postprocessing amends the coefficients that changed after recompression to ensure the robustness of unmodified coefficients. Additionally, we devise heuristics to mitigate post-processing and explore the relationship between the modification magnitude and distortion assignment with novel scaling functions, which all contribute to security. The experimental results demonstrate that PMAS is competent for high quality images, and the effect of every module in PMAS is verified.

Adaptive Information Hiding Method Based on Feature Extraction for Visible Light Communication

Visible Light Communication (VLC) is an emerging short-range optical communication technology that can alleviate spectrum congestion. However, VLC faces security problems with man-in-the-middle hijacking and bypass listening. Since the covert transmission of visible light information has the characteristics of strong concealment and difficult detection, VLC based on information hiding becomes a new paradigm to solve these security problems. This paper presents a method in which Red, Green, Blue (RGB) Light-emitting Diode (LED) is used to secure the VLC by using the information hiding technique. Specifically, a gammatone filter bank-based feature extraction method is suggested. This method extracts robust vector for information hiding and extraction. To hide and extract the information, a dither modulation information hiding scheme based on the adaptive optimization is proposed. The presented method uses dither modulation to hide information in the extracted feature vector, which can effectively improve the success rate of information extraction. On the basis of performance analysis, the superiority of the presented method is illustrated by comparing with the existing methods.

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.

Adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm.

How to improve the robustness to resist attacks and how to adaptively match the key parameters of the watermarking algorithm with the performance requirements to achieve the best performance in different applications are two hot issues in the research of audio watermarking algorithms. An adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm (BOA) is proposed. Based on the convolution operation, a stable feature is designed to carry the watermark, which will improve the robustness by means of the stability of this feature to prevent the watermark loss. Blind extraction will be achieved only by comparing the feature value and the quantized value without the original audio. The BOA is used to optimize the key parameters of the algorithm which can be matched with the performance requirements by coding the population and constructing the fitness function. Experimental results confirm that this proposed algorithm can adaptively search for the optimal key parameters that match the performance requirements. Compared with other related algorithms in recent years, it exhibits strong robustness against various signal processing attacks and synchronization attacks.

New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters

In image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure. The present paper aims to propose a new blind watermarking method based on the dither modulation by developing an automatic selection of the optimum embedding parameters that guarantee high-quality watermarked images and low bit error rates during the extraction process. The proposed automatic search method for the best discrete moments subsets is based on an evolutionary algorithm and adopts a specific coding strategy with a group of genes representing the embedding positions. The second part of the chromosome is reserved for the embedding strength coding, followed by the application of different evolutionary operators on the evolution pool. Our study explores the impact of maximum generation, population size, and cutting-point positions with different crossover and mutation rates. The performances under different attack conditions are evaluated, and a comparative study is established with other conventional selection methods and other discrete transforms. Results show that our proposed optimization algorithm baptized EWIMps achieves the best trade-off between robustness and imperceptibility with a peak signal-to-noise ratio varying from 28.33 dB to 59.87 dB and a normalized cross-correlation value from 0.707 to 1.

Adversarial robust image steganography against lossy JPEG compression

Traditional image steganography assumes that the transmission channel is lossless, thus it can not assure the correct extraction of hidden secret messages in real social networks. Recently, some robust image steganographic methods have been proposed to resist lossy JPEG compression. In this paper, we first introduce adversarial mechanism into robust image steganography to enhance steganography security. Instead of modifying embedding costs in traditional adversarial embedding steganography, we propose adversarial dither modulation algorithm according to the cover gradients calculated from a pre-trained CNN (Convolutional Neural Networks)-based steganalyzer. This algorithm determines the dither directions via the signs and magnitudes of the cover gradients. In addition, we carefully select proper embedding elements according to the prior information of the transmission channel to obtain a good tradeoff between “robustness” and “security” for robust steganography. Experimental results show that the proposed method can achieve significant improvements on security while preserving very good robustness compared with related work.

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.

Wavelength-Tunable OTDR for DWDM-PON Based on Optimized Wavelet Denoising

In this letter, a wavelength-tunable Optical Time-Domain Reflectometer (OTDR) for Dense Wavelength Division Multiplexing Passive Optical Network (DWDM-PON) is proposed and experimentally demonstrated. An Integrated Tunable Laser Assembly (ITLA) serves as the light source permitting measurements of 80 DWDM channels in the C-band. Dither modulation is added to ITLA to depress coherent noise resulted from the narrow linewidth of the light source. Semiconductor Optical Amplifier (SOA) modulates the continuous light into pulsed light and meanwhile amplifies peak optical power of the pulsed light to 13 dBm. In addition, we optimize the wavelet denoising algorithm for further signal-noise ratio (SNR) enhancement. Dynamic range and spatial resolution of the proposed OTDR reach up to 16.2 dB and 2 m respectively. A DWDM-PON fiber link including an Arrayed Waveguide Grating (AWG) is measured to verify the system performances, and the OTDR profiles of different channels coincide with each other. The designed wavelength-tunable OTDR is shown to be appropriate for DWDM-PON.

Imperceptible–visible watermarking for copyright protection of digital videos based on temporal codes

Digital watermarking has emerged as an important tool for copyright protection of digital videos. In this paper, we introduce a robust and imperceptible video watermarking method performed in the base-band domain that is not computationally expensive. Two nonoverlapped watermarks are embedded in the host video frames. The first watermark is a grayscale image that represents the ownership of the video. First, this watermark is decomposed into a sequence of binary images called temporal codes by using a spatiotemporal masking model. Then, the temporal codes are embedded in the spatial domain of video frames by using the imperceptible–visible watermarking paradigm. Since it is well known that such a paradigm is computationally expensive, we employ a technique that considers the video scene as the basic processing unit to considerably increase the execution speed. The second watermark works as a supporting media to share data between embedding and exhibition stages and is embedded in the discrete cosine transform (DCT) with a very robust method based on quantization index modulation (QIM) under dither modulation and some spatiotemporal criteria of the human visual system (HVS). Computer simulations were conducted regarding robustness, imperceptibility, and time consumption to determine the feasibility of our proposal. Experimental results confirm that the combination of temporal codes and the imperceptible–visible watermarking paradigm is an innovation in the field that brings advantages such as simplicity, low computational complexity, and improved robustness and 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.

Use of SHDM in commutative watermarking encryption

SHDM stands for Sphere-Hardening Dither Modulation and is a watermarking algorithm based on quantizing the norm of a vector extracted from the cover work. We show how SHDM can be integrated into a fully commutative watermarking-encryption scheme and investigate implementations in the spatial, DCT, and DWT domain with respect to their fidelity, robustness, capacity, and security of encryption. The watermarking scheme, when applied in the DCT or DWT domain, proves to be very robust against JPEG/JPEG2000 compression. On the other hand, the spatial domain-based approach offers a large capacity. The increased robustness of the watermarking schemes, however, comes at the cost of rather weak encryption primitives, making the proposed CWE scheme suited for low to medium security applications with high robustness requirements.

Robust Adaptive Steganography Based on Dither Modulation and Modification With Re-Compression

Traditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous works utilize reverse engineering and compression-resistant domain constructing to implement robust adaptive steganography. In this paper, we adopt modification with re-compression scheme to improve the robustness of stego sequences in stego images. To balance security and robustness, we move the embedding domain to the low frequency region of DCT (Discrete Cosine Transform) coefficients to improve the security of robust adaptive steganography. In addition, we add additional check codes to further reduce the average extraction error rate based on the framework of E-DMAS (Enhancing Dither Modulation based robust Adaptive Steganography). Compared with GMAS (Generalized dither Modulation based robust Adaptive Steganography) and E-DMAS, experiment results show that our scheme can achieve strong robustness and improve the security of robust adaptive steganography greatly when the channel quality factor is known.

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.

A novel attention-guided JND Model for improving robust image watermarking

Just noticeable distortion (JND) and visual attention (VA), which are two widely used mathematical models of human visual system (HVS) that aim to simulate the human brain mechanism, are sufficiently explored and applied to many researches including digital watermarking. The activity of human brain, however, is extremely complex and it can be more limited due to complicated fusion of spatial saliency for image domain. In this paper, we propose a novel VA guided JND model in which we fuse the final attention map from the low-level features by using two laws of Gestalt principle. Firstly, we demonstrate a classic JND model in DCT domain, which consists of spatial contrast sensitivity function (CSF), luminance adaptation (LA) and contrast masking (CM). The foveation effect and orientation feature are considered to obtain the CSF and CM factor. The foveation effect is affected by spatial attention, and the orientation features are modeled for CM effect together with traditional block texture strength through three direction-based AC coefficients in DCT domain. The attention features are integrated with a novel Gestalt principle-based weighting mechanism for the final block-based VA model, which is then used to modulate JND profiles with two non-linear functions. Finally, the proposed VA-guided JND model is incorporated into a logarithmic spread transform dither modulation (L-STDM) watermarking scheme. Experimental results show that the newly proposed algorithm can achieve good performance in term of robustness and get better visual quality.

Gram–Schmidt Orthogonalization-Based Audio Multiple Watermarking Scheme

This paper proposes an audio multi-watermarking scheme based on Gram–Schmidt orthogonalization. A random signal generator is applied to the host audio signal to generate the target segment for the local watermarking, thus improving the imperceptibility compared with the conventional global watermarking. The discrete cosine transform low-frequency coefficients of high stability are selected as watermark embedder, to ensure the robustness of this scheme. Consequently, the Gram–Schmidt orthogonalization process is employed to generate a set of orthogonal vectors, into which the spread transform dither modulation is applied to, respectively, embed the multiple watermark messages simultaneously. The orthogonality ensures that multiple watermark messages can be independently extracted without compromising the robustness against attacks. By embedding the multiple watermark messages into the same segment of the host audio signal, the watermark embedding capacity can be greatly enhanced while preserving the imperceptibility and robustness. A variety of experiments are conducted, and the results indicate the good performance of the proposed scheme. The proposed scheme has demonstrated superior performance gains over the state-of-the-art methods.

Frame-synchronous Blind Audio Watermarking for Tamper Proofing and Self-Recovery

This paper presents a lifting wavelet transform (LWT)-based blind audio watermarking scheme designed for tampering detection and self-recovery. Following 3-level LWT decomposition of a host audio, the coefficients in selected subbands are first partitioned into frames for watermarking. To suit different purposes of the watermarking applications, binary information is packed into two groups: frame-related data are embedded in the approximation subband using rational dither modulation; the source-channel coded bit sequence of the host audio is hidden inside the 2nd and 3rd -detail subbands using 2N-ary adaptive quantization index modulation. The frame-related data consists of a synchronization code used for frame alignment and a composite message gathered from four adjacent frames for content authentication. To endow the proposed watermarking scheme with a self-recovering capability, we resort to hashing comparison to identify tampered frames and adopt a Reed–Solomon code to correct symbol errors. The experiment results indicate that the proposed watermarking scheme can accurately locate and recover the tampered regions of the audio signal. The incorporation of the frame synchronization mechanism enables the proposed scheme to resist against cropping and replacement attacks, all of which were unsolvable by previous watermarking schemes. Furthermore, as revealed by the perceptual evaluation of audio quality measures, the quality degradation caused by watermark embedding is merely minor. With all the aforementioned merits, the proposed scheme can find various applications for ownership protection and content authentication.

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 adaptive steganography based on generalized dither modulation and expanded embedding domain

Sharing images on social network platforms (SNPs) from mobile intelligent devices is becoming more and more popular and has great potential for covert communication. However, images will be processed by lossy social network channels, such as JPEG compression, which reduces image quality and destroys message extraction. Previous robust steganographic schemes using reverse engineering or anti-compression domain for SNPs suffer from some security flaws or have only small capacity and low security level. The purpose of this paper is to refine the robust steganographic scheme by considering asymmetric costs for different modification polarities and expanding the embedding domain for digital images, aiming to aggregate the modifications on the elements with small costs. Such a new strategy that utilizes asymmetric distortion for dither modulation to implement ternary embedding can be regarded as generalized dither modulation in substantial sense. Compared with the original Dither Modulation-based robust Adaptive Steganography (DMAS), the proposed scheme selects more DCT coefficients as cover elements and we call it Generalized dither Modulation-based robust Adaptive Steganography (GMAS). Extensive experiments demonstrate that the proposed GMAS gains significant performance improvements in terms of robustness and security when compared with DMAS.