Quantization Index Modulation

We present a new paradigm for the insertion of multiple watermarks in images. Instead of an explicitly defined embedding process, the watermark embedding is achieved implicitly by determining a feasible image meeting multiple desired constraints. The constraints are designed to ensure that the watermarked image is visually indistinguishable from the original and produces a positive detection result when subjected to detectors for the individual watermarks even in the presence of signal processing operations, particularly compression. We develop useful mathematical definitions of constraint sets for different visual models, for transform domain compression, and for both spread-spectrum and quantization index modulation (QIM) watermark detection scenarios. Using the constraints with a generalized vector space projections method (VSPM), we determine a watermarked signal. Experimental results demonstrate the flexibility and usefulness of the presented methodology in addressing multiple watermarking scenarios while providing implicit shaping of the watermark power to meet visual requirements.

The Dirty Paper Trellis Code (DPTC) watermarking scheme [1] is a very efficient high rate scheme. It has however a major drawback: its computational complexity. This problem is addressed by using a faster embedding technique. The embedding space is built by projecting some wavelet coefficients onto secret carriers. The fast embedding is achieved with a dichotomous rotation in the Cox, Miller, and Bloom plane. Besides, a modern watermarking scheme should manage the psychovisual impact due to the watermarking signal. This is addressed by using a psychovisual mask. Our low complexity watermarking scheme is compared to two other psychovisual low complexity approaches and results show a good behavior in terms of robustness. The obtained results give a very clear vision, in realistic conditions of use, of the current state-of-the-art for high-rate watermarking schemes of low complexity.

This paper develops a unified information-theoretic framework for steganography that simultaneously addresses four fundamental aspects of secure communication: reliability, statistical undetectability, computational security, and robustness under noisy channel conditions. While classical studies-such as those by Cachin, Moulin and O'Sullivan, and subsequent work on steganographic capacity-have analyzed these aspects in isolation or under highly idealized assumptions (e.g., perfect security or specific cover models), our contribution is to formalize them within a single, coherent theoretical model. We derive general definitions and constraints for steganographic systems in terms of statistical distances (total variation, Kullback-Leibler divergence, and f-divergences) and provide capacity bounds and impossibility results that hold under joint reliability and security requirements. Furthermore, we extend the analysis to scenarios involving computationally bounded adversaries and robust decoding over noisy channels, highlighting the trade-offs that emerge between payload size, detectability, and error probability. The resulting theory unifies and extends several classical results in steganography and covert communication, offering a rigorous mathematical foundation that connects information-theoretic limits with practical security considerations. This framework not only clarifies the fundamental boundaries of steganographic communication but also provides guidelines for the design of future embedding schemes that balance security, robustness, and efficiency.

In this paper we present a novel source separation method aiming to overcome the difficulty of modelling non-stationary signals. The method can be applied to mixtures of musical instruments with frequency and/or amplitude modulation, e.g. typically caused by vibrato. It is based on a signal representation that divides the complex spectrogram into a grid of patches of arbitrary size. These complex patches are then processed by a two-dimensional discrete Fourier transform, forming a tensor representation which reveals spectral and temporal modulation textures. Our representation can be seen as an alternative to modulation transforms computed on magnitude spectrograms. An adapted factorization model allows to decompose different time-varying harmonic sources based on their particular common modulation profile: hence the name Common Fate Model. The method is evaluated on musical instrument mixtures playing the same fundamental frequency (unison), showing improvement over other state-of-the-art methods.

This paper introduces a novel technique for reconstructing the phase of modified spectrograms of audio signals. From the analysis of mixtures of sinusoids we obtain relationships between phases of successive time frames in the Time-Frequency (TF) domain. Instantaneous frequencies are estimated locally to encompass the class of non-stationary signals such as vibratos. This technique ensures the horizontal coherence (over time) of the partials. The method is tested on a variety of data and demonstrates better performance than traditional consistencybased approaches. We also introduce an audio restoration framework and obtain results that compete with other state-of-theart methods. Finally, we apply this phase recovery method to an audio source separation task where the spectrograms of the isolated components are known. We propose to use the phase unwrapping estimate to initialize a source separation iterative procedure. Experiments conducted on realistic music pieces demonstrate the effectiveness of such a method for various music signal processing tasks.

Source separation, which consists in decomposing data into meaningful structured components, is an active research topic in many areas, such as music and image signal processing, applied physics and text mining. In this paper, we introduce the Positive α-stable (PαS) distributions to model the latent sources, which are a subclass of the stable distributions family. They notably permit us to model random variables that are both nonnegative and impulsive. Considering the Lévy distribution, the only PαS distribution whose density is tractable, we propose a mixture model called Lévy Nonnegative Matrix Factorization (Lévy NMF). This model accounts for low-rank structures in nonnegative data that possibly has high variability or is corrupted by very adverse noise. The model parameters are estimated in a maximum-likelihood sense. We also derive an estimator of the sources given the parameters, which extends the validity of the generalized Wiener filtering to the PαS case. Experiments on synthetic data show that Lévy NMF compares favorably with state-of-the art techniques in terms of robustness to impulsive noise. The analysis of two types of realistic signals is also considered: musical spectrograms and fluorescence spectra of chemical species. The results highlight the potential of the Lévy NMF model for decomposing nonnegative data.

Audio watermarking is a technique, which can be used to embed information into the digital representation of audio signals. The main challenge is to hide data representing some information without compromising the quality of the watermarked track and at the same time ensure that the embedded watermark is robust against removal attacks. Especially providing perfect audio quality combined with high robustness against a wide variety of attacks is not adequately addressed and evaluated in current watermarking systems. In this paper, we present a new phase modulation audio watermarking technique, which among other features provides evidence for high audio quality. PSK modulation has been proposed as an effective approach to watermarking.

This paper considers watermarking detection, also known as zero-bit watermarking. A watermark, carrying no hidden message, is inserted in content. The watermark detector checks for the presence of this particular weak signal in content. The paper aims at looking to this problem from a classical detection theory point of view, but with side information enabled at the embedding side. This means that the watermarking signal is a function of the host content. Our study is twofold. The first issue is to design the best embedding function for a given detection function (a Neyman-Pearson detector structure is assumed). The second issue is to find the best detection function for a given embedding function. This yields two conditions, which are mixed into one 'fundamental' differential equation. Solutions to this equation are optimal in these two senses. Interestingly, there are other solutions than the regular quantization index modulation scheme. The JANIS scheme, for instance, invented in a heuristic manner several years ago, is justified as it is one of these solutions.

This paper addresses an efficient technique to embed and detect digital fingerprint code. Orthogonal modulation method is a straightforward and widely used approach for digital fingerprinting but shows several limitations in computational cost and signal efficiency. Coded modulation method can solve these limitations in theory. However it is difficult to perform well in practice if host signals are not available during tracing colluders, other kinds of attacks are applied, and the size of fingerprint code becomes large. In this paper, we propose a hybrid modulation method, in which the merits of orthogonal modulation and coded modulation method are combined so that we can achieve low computational cost and high signal efficiency. To analyze the performance, we design a new fingerprint code based on GD-PBIBD theory and modulate this code into images by our method using spread-spectrum watermarking on frequency domain. The results show that the proposed method can efficiently handle large fingerprint code and trace colluders against averaging attacks.

MPEG-1 Layer 3 (MP3) is one of the most popular compression formats used for sound and especially for music. However, during the coding process, the MP3 algorithm negatively affects the spectral and dynamic characteristics of the audio file being compressed. The aim of this study is to evaluate the effect the MP3 coding format for different music genres and different bitrates via listening tests in which the original uncompressed files and the compressed files are compared. For this purpose, five different music genres were selected (rock, jazz, electronic, classical and solo instrument), and the files were compressed in three different bitrates (96 kbps, 160 kbps and 320 kbps). The semantic differential method was used, and ten bipolar scales were selected for the listening tests (e.g., better-worse, more distortion-less distortion, etc.). The following are the most important findings of this study: classical music was negatively affected the most among the genres due to the MP3 compression (lowest ratings in 8 out of 10 bipolar scales), the solo instrument was least affected among the genres (highest rating in 7 out of 10 bipolar scales), and for higher bit rates, the differences in ratings were small for all music genres. The findings of this study could be used to optimize and adapt the standard, depending on the music genre and the musical piece that needs to be encoded.

We address the issue of underdetermined source separation in a particular informed configuration where both the sources and the mixtures are known during a so-called encoding stage. This knowledge enables the computation of a side-information which is small enough to be inaudibly embedded into the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the extracted sideinformation are processed for source separation. The proposed system models the sources as independent and locally stationary Gaussian processes (GP) and the mixing process as a linear filtering. This model allows reliable estimation of the sources through generalized Wiener filtering, provided their spectrograms are known. As these spectrograms are too large to be embedded in the mixtures, we show how they can be efficiently approximated using either Nonnegative Tensor Factorization (NTF) or image compression. A high-capacity embedding method is used by the system to inaudibly embed the separation side-information into the mixtures. This method is an application of the Quantization Index Modulation technique applied to the time-frequency coefficients of the mixtures and permits to reach embedding rates of about 250 kbps. Finally, a study of the performance of the full system is presented.

We define protocol coding as a way to encode information in the actions taken by a communication protocol. In this work we investigate strategies for protocol coding via combinatorial ordering of the labelled user resources (packets, channels) in an existing, primary system. This introduces a new, secondary communication channel in the existing system, which has been considered in the prior work exclusively in a steganographic context. Instead, we focus on the use of secondary channel for reliable communication with newly introduced secondary devices, that are low-complexity versions of the primary devices, capable only to decode the robustly encoded header information in the primary signals. We introduce a suitable communication model, capable to capture the constraints that the primary system puts on protocol coding. We derive the capacity of the secondary channel under arbitrary error models. The insights from the information-theoretic analysis are used to design practical errorcorrecting schemes for secondary channels based on trellis codes.

This paper presents a blind, adaptive quantization index modulation (QIM)-and singular value decomposition (SVD)-based watermarking scheme to embed watermark bits in the approximation subband of the wavelet domain. The QIM technique adaptively determines the quantization step for each embedding block using a statistical model. The SVD technique uses the quantization step to modify the SVs of each embedding block. This modification ensures the SVs of highly textured blocks are largely modified and the SVs of smooth textured blocks are slightly modified. The successive packing interleaving (SPI) scheme and the one-way hashing functions are respectively applied to improve the robustness of the proposed system. A statistical-clustering method is also applied offline to decide two optimal weighting parameters for the QIM technique so the adaptive quantization steps are optimal for all embedding blocks. Experimental results demonstrate our scheme is robust against JPEG compressions down to a level of 20% quality factor. It also performs better than a peer SVD-based wavelet domain watermarking approach.

Video data hiding is still an important research topic due to the design complexities involved. We propose a new video data hiding method that makes use of erasure correction capability of Repeat Accumulate codes and superiority of Forbidden Zone Data Hiding. Selective embedding is utilized in the proposed method to determine host

Probability-of-error exponents have recently been derived for watermarking systems based on spread-spectrum and quantization-index modulation methods. This paper takes this work one step further and presents minmax error exponents for any embedding scheme and any attack (subject to distortion constraints) at all rates below capacity. The decoders used are universal: they do not know the attack used. Randomized codes outperform deterministic codes, except in the case of memoryless attacks where the same performance is obtained using either kind of code.

The Cloud Computing is a dynamic term which provides dispute free data outsourcing facility which prevent the user from burden of local storage issues. However, the biggest issues to be focused are on providing secure & reliable data archive over unreliable service providers. In this paper, we focus on using watermarking technology of copyright protection for Cloud Computing, Using GLCM & PCA algorithm to extract features of original image and to generate semi blind watermarking image. The collaboration of Digital Watermarking when used for Cloud Computing can significantly result to make the system robust as well as secure user’s data.

A scheme for energy compensation is proposed to remedy the deficiency of a DWTDCT-based scheme while applying quantization index modulation (QIM) to perform blind audio watermarking. Our experimental results show that both compensated and uncompensated DWTDCT schemes can achieve satisfactory robustness and imperceptibility at a payload capacity as high as 516.80 bps. However, because of the exploitation of the auditory masking effect, the perceptual quality attained by the compensated DWT-DCT scheme is even higher than that by the uncompensated one. With the employment of energy compensation, not only a 100% recovery of the watermark is guaranteed for non-attack situations but the survival rate is substantially improved in the case of extremely lowpass filtering. Furthermore, in a comparison with four other recently developed methods, the proposed DWT-DCT scheme observably exhibits a superior performance in imperceptivity and payload capacity while its robustness is comparable with ...

This paper presents a novel approach for blind audio watermarking. The proposed scheme utilizes the flexibility of discrete wavelet packet transformation (DWPT) to approximate the critical bands and adaptively determines suitable embedding strengths for carrying out quantization index modulation (QIM). The singular value decomposition (SVD) is employed to analyze the matrix formed by the DWPT coefficients and embed watermark bits by manipulating singular values subject to perceptual criteria. To achieve even better performance, two auxiliary enhancement measures are attached to the developed scheme. Performance evaluation and comparison are demonstrated with the presence of common digital signal processing attacks. Experimental results confirm that the combination of the DWPT, SVD, and adaptive QIM achieves imperceptible data hiding with satisfying robustness and payload capacity. Moreover, the inclusion of self-synchronization capability allows the developed watermarking system to withstand time-shifting and cropping attacks.

JPEG has played an important role in the image compression field for the past two decades. Quantization tables in the JPEG scheme is a key factor that is responsible for compression/quality trade-off. Finding the optimal quantization table is an open research problem. Studies recommend the genetic algorithm to generate the optimal solution. Recent reports revealed optimal quantization table generation based on a classical genetic algorithm (CGA). Although the CGA produces better results, it shows inefficiency in terms of convergence speed and productivity of feasible solutions. This paper proposes a knowledge-based genetic algorithm (KBGA), which combines the image characteristics and knowledge about image compressibility with CGA operators such as initialization, selection, crossover, and mutation for searching for the optimal quantization table. The experimental results show that the optimal quantization table generated using the proposed KBGA outperforms the default JPEG quantization table in terms of mean square error (MSE) and peak signal-to-noise ratio (PSNR) for target bits per pixel. The KBGA was also tested on a variety of images in three different bits values per pixel to show its strength. The proposed KBGA produces an average PSNR gain of 3.3% and average MSE gain of 20.6% over the default JPEG quantization table. The performance measures such as average unfitness value, likelihood of evolution leap, and likelihood of optimality are used to validate the efficacy of the proposed KBGA. The novelty of the KBGA lies in the number of generations used to attain an optimal solution as compared to the CGA. The validation results show that this proposed KBGA guarantees feasible solutions with better quality at faster convergence rates.

Compression of audio signal is one of the basic technologies in multimedia. This paper describes about Daubechie wavelet based audio compression. This method uses optimal wavelet selection and wavelet coefficients quantization procedure together with different Companding methods like A-law and U-law Compandings. This paper presents a technique to incorporate psychoacoustic models into an adaptive wavelet packet scheme to achieve perceptually transparent compression of high-quality audio signals [1].

Sensitive information being shared on the internet is growing. Becauseof this, it is increasingly necessary to take security measureswhilst this information travels in the network. Digital steganographyallows one to send sensitive information in a hidden manner.Although there is a plethora of techniques for such a goal, findingan appropriate one is not always simple. This paper implementsand compares spatial-domain digital steganography techniques inboth RGB and grayscale images. A frequency-domain heuristic forreducing the visual impact of digital steganography in grayscaleimages is presented. As another result of this work, a dataset is alsoavailable in the Kaggle platform with 18 GB of images, containingsecret messages using the techniques under study. In addition, aPython language library was also made available in the PyPI repository,allowing for both concealment and revelation of messagesusing the presented digital steganography methods.

With the ever-increasing popularity of LiDAR (Light Image Detection and Ranging) sensors, a wide range of applications such as vehicle automation and robot navigation are developed utilizing the 3D LiDAR data. Many of these applications involve remote guidance-either for safety or for the task performance-of these vehicles and robots. Research studies have exposed vulnerabilities of using LiDAR data by considering different security attack scenarios. Considering the security risks associated with the improper behavior of these applications, it has become crucial to authenticate the 3D LiDAR data that highly influence the decision making in such applications. In this paper, we propose a framework, ALERT (Authentication, Localization, and Estimation of Risks and Threats), as a secure layer in the decision support system used in the navigation control of vehicles and robots. To start with, ALERT tamper-proofs 3D LiDAR data by employing an innovative mechanism for creating and extracting a dynamic watermark. Next, when tampering is detected (because of the inability to verify the dynamic watermark), ALERT then carries out cross-modal authentication for localizing the tampered region. Finally, ALERT estimates the level of risk and threat based on the temporal and spatial nature of the attacks on LiDAR data. This estimation of risk and threats can then be incorporated into the decision support system used by ADAS (Advanced Driver Assistance System). We carried out several experiments to evaluate the efficacy of the proposed ALERT for ADAS and the experimental results demonstrate the effectiveness of the proposed approach. CCS CONCEPTS • Computer systems organization → Real-time systems; Robotics; • Security and privacy → Network Security;

Blind watermarking utilized the quantization table to embed the watermark by adding or subtracting a quantized constant value. When the watermarked image processes malicious manipulations the constant quantization table is not robust. In this paper, a new adaptive and duplicate quantized blind watermarking system is proposed to attend higher transparency. We use an adaptive quantization table to advance the robustness according to the properties of the human visual system and just noticeable distortion. And the error-correcting based watermarking will have the property that corrects errors of the extracted watermark automatically. A multiresolution watermarking based on the wavelet transformation is selected and therefore it can resist the destruction of image processing. In our experiments, the results show that the robustness and transparency of the adaptive watermarking is much better than the traditional one.

A new robust watermarking method, named SFLA-QIM, is proposed based on the shuffled frog leaping algorithm (SFLA) and quantization index modulation (QIM). The shuffled frog leaping algorithm is utilized to find out the optimal embedding position and adaptive quantization step for embedding watermark into a carrier image in the framework of QIM. A carefully chosen fitness function is designed in terms of the Peak Signal to Noise Ratio (PSNR) and the Normalized Correlation (NR) value to achieve high transparency and robustness. The proposed scheme is blind. Compared with other quantization index related watermarking methods, SFLA-QIM exhibits satisfactory robustness against a wide variety of attacks such as amplitude scaling, filtering, noise addition, cropping and JPEG compression.

A perceptual audio coder typically consists of a lterbank which breaks the signal into its frequency components. These components are then quantized using a perceptual masking model. Previous eorts have indicated that a high resolution lterbank, e.g., the modied discrete cosine transform (MDCT) with 1024 subbands, is able to minimize the bit rate requirements for most of the music samples. The high resolution MDCT ,h o wever, is not suitable for the encoding of non-stationary segments of music. A long/short resolution or \window" switching scheme has been employed to overcome this problem but it has certain inherent disadvantages which become prominentatl o wer bit rates (< 64 kbps for stereo). We propose ano vel switched lterbank scheme which switches between a MDCT and a wavelet lterbank based on signal characteristics. A tree structured wavelet lterbank with properly designed lters oers natural advantages for the representation of non-stationary segments such as attacks. Furthermore, it allows for the optimum exploitation of perceptual irrelevancies.

This paper presents a hybridization of neural networks and multiobjective memetic optimization for an adaptive, robust, and perceptual data hiding method for colour images. The multiobjective optimization problem of a robust and perceptual image data hiding is introduced. In particular, trade-off factors in designing an optimal image data hiding to maximize the quality of watermarked images and the robusteness of watermark are investigated. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for the efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of GRNN are searched by a MOMA. The experimental results show that the propsed approach achieves adaptation, robustness, and imperceptibility in image data hiding.

In this paper, a robust and blind speech watermarking technique is proposed using a combined scheme based on discrete cosine transform (DCT) and discrete wavelet transform (DWT) Algorithms within signal sub-sampling. To achieve good imperceptibility, hybridization is implemented against many attacks such as: re-quantization, cropping, echo, amplification and additive white Gaussian noise (AWGN). We profit from the advantages of correlation between two successive samples so that we used signal sub-sampling. Obtained results, compared successfully to recent researches, show the robustness of our proposed approach.

This paper 12 presents a scheme for scalable, error resilient coding of audio signals. The scheme is implemented here as part of a subband-based audio codec and involves the union of two interconnected ideas: Tree-structured vector quantization (TSVQ) and reallocation of bits (RoB). The former ensures that the encoding procedure results in fixed-length codes while the latter is a low-complexity scaling algorithm that computes optimal bitrates for each subband when the bitstream needs to be downscaled to a lower bitrate. The functionality enabled by these two systems assumes great importance when the encoded material is to be delivered over channels and networks that have a time-varying bitrate, such as IP-based wireless networks. Such channels are often error-prone and the potentially debilitating effects of introduced errors are mitigated by the inherent fixed-length property of all codewords. We present results that highlight the performance of this system under noisy conditions and show that the degradation in quality is graceful.

En la actualidad, la sociedad demanda que su información, en la mayoría de las veces, confidencial, sea protegida de cualquier agente externo no autorizado a acceder a ella. Por tal razón se están desarrollando técnicas matemáticas e informáticas para impedir que la información sensible sea accedida, modificada, plagiada o destruida. En este trabajo, se presenta un algoritmo esteganográfico para la seguridad de la información privada y confidencial que transita por los diferentes canales de comunicación en el Internet. El método propuesto está vinculado al dominio de la frecuencia, el cual emplea un par de claves de 64 bits, una pública y una privada, para luego ocultar la información secreta en algunos coeficientes de las componentes de baja frecuencia. Finalmente, se presenta un análisis experimental para medir la calidad, imperceptibilidad y seguridad del sistema esteganográfico propuesto. PALABRAS CLAVES: Algoritmo esteganográfico; dominio de la frecuencia; imágenes digitales; seguridad.