Audio Watermarking Research Articles

Copyright Protection Based on Hybrid Image Watermark (DCT) with Audio Watermark (EMD)

A digital watermark is a type of security that incorporates data into a signal, making it difficult to erase and showing ownership and validity. An important issue brought about by the growing use of the Internet is illicit copyright infringement and authentication. Previous research has proposed several techniques for this problem but has proven them to be either robust, difficult to detect, or fragile and easy to break. Because of this, in this paper, we use two separate kinds of watermarks—one for image and one for audio—to merge these two purposes into one. The image watermark discrete cosine transform (DCT) is renowned for its robustness. It is embedded in an audio watermarking algorithm based on empirical mode decomposition (EMD), ensuring a safe and fragile embedding process. The audio signal was divided into frames, and each one was decomposed by EMD into intrinsic mode functions (IMFs). The DCT watermark bits and the synchronization codes are embedded into the extrema of the first IMF, a high frequency mode sensitive to any small changes in the audio signal. The experimental outcomes suggest that while using two separate kinds of watermarks may seem like a comprehensive solution, it shows the flexibility of EMD and the limits of the DCT watermark technique in different scenarios.

An Audio Watermarking Algorithm Based on Adversarial Perturbation

Recently, deep learning has been gradually applied to digital watermarking, which avoids the trouble of hand-designing robust transforms in traditional algorithms. However, most of the existing deep watermarking algorithms use encoder–decoder architecture, which is redundant. This paper proposes a novel audio watermarking algorithm based on adversarial perturbation, AAW. It adds tiny, imperceptible perturbations to the host audio and extracts the watermark with a pre-trained decoder. Moreover, the AAW algorithm also uses an attack simulation layer and a whitening layer to improve performance. The AAW algorithm contains only a differentiable decoder, so it reduces the redundancy. The experimental results also demonstrate that the proposed algorithm is effective and performs better than existing audio watermarking algorithms.

Imperceptible backdoor watermarks for speech recognition model copyright protection

As the demand for shared pre-trained deep neural network models continues to rise, safeguarding the intellectual property of models is also increasingly significant. While existing studies predominantly concentrate on protecting pre-trained image recognition models, limited research covers pre-trained speech recognition models. In this paper, we propose the black-box watermark method to authenticate the ownership of speech recognition models. This method can mitigate the risk of unauthorized AI services being created by attackers who gain access to the pre-trained model. Accordingly, we present three watermarking methods: Gaussian noise watermark, extreme frequency Gaussian noise watermark, and unrelated audio watermark. These generated watermarks, embedded into models through training or fine-tuning, exhibit remarkable fidelity and effectiveness, backed by rigorous experimental validation. Furthermore, our experiments reveal that the extreme frequency noise backdoor enhances the robustness of the watermark compared to the Gaussian noise and unrelated audio watermark.

Legal Protection For Unauthorized Copying Of Songs On Digital Platforms Through Audio Watermarking Method

The objective of this research is to investigate the effectiveness of the Copyright Law in safeguarding digital songs with audio watermarking from illegal duplication, and to analyze the legal responsibilities of those who duplicate songs on digital platforms using such methods under the Copyright Law. The research methodology employed is normative juridical, focusing on the internal aspects of positive law and utilizing literature sources like journals, books, case law, and relevant legislative materials, particularly Law No. 28 of 2014 on Copyright. Legal materials are gathered through a literature review as a secondary data source, along with descriptive analysis to elucidate the legal implications concerning copyright protection in the digital age. The novelty of this research lies in the integration of advanced and effective audio watermarking technology in addressing the challenges of copyright protection in the digital era. The research results highlight the importance of copyright protection in the music industry in the digital era. Audio watermarking methods are used to insert data into digital content to protect content ownership and identify copyright holders. There are efforts to overcome the challenges in preventing illegal acquisition of copyrighted content by utilising watermarking technology. It can thus be concluded that despite efforts made to regulate the digital marketplace and improve legal standards, illegal distribution of material is still quite rampant. YouTube is cited as an effective platform in protecting intellectual property rights by successfully addressing the issue of copyright infringement by removing infringing content.

Survey of imperceptible and robust digital audio watermarking systems

Survey of imperceptible and robust digital audio watermarking systems

A lightweight fragile audio watermarking method using nested hashes for self-authentication and tamper-proof

A lightweight fragile audio watermarking method using nested hashes for self-authentication and tamper-proof

A novel SVD-based adaptive robust audio watermarking algorithm

A novel SVD-based adaptive robust audio watermarking algorithm

MP3 Audio watermarking using calibrated side information features for tamper detection and localization

MP3 Audio watermarking using calibrated side information features for tamper detection and localization

Transient detection-based adaptive audio watermarking using attack-aware optimization

There has been a continued need for technology to prevent the illegal use of music in online transactions and protect the rights of copyright holders. Recently, the types of attacks have become very diverse due to various online environments and content production methods. Therefore, an adaptive protection method aware of content and attacks is needed. This paper proposes a robust watermarking algorithm of a blind method to protect audio copyright in online streaming services. After detecting the transient points of the audio and processing the signals, they are used as reference triggers for frequency conversion. Transient triggers should be determined to be detected even after an attack. Audio samples are converted into the fast Fourier transform (FFT) frequency coefficients, and a watermark is embedded using the quantization index modulation (QIM) method. Various user-defined parameters can be adjusted during the watermark embedding process. Such parameters may be determined through experiments or an optimization process by targeting both the quality of the watermarked audio and the bit error rate (BER) of the watermark extracted after the attack. A watermark can be extracted using a similar process using parameters used in watermark embedding. If there is no information about the parameters used in embedding, the watermark can be extracted by tracking the minimum BER of the extracted watermark. Compared to the existing methods, the proposed watermarking technique showed excellent performance against most attacks and superior performance for filtering, shifting, and moving picture expert group (MPEG)-2 audio layer 3 (MP3) compression attacks. Therefore, the proposed method is likely a good solution for protecting audio copyright in an online streaming environment.

High capacity, secure audio watermarking technique integrating spread spectrum and linear predictive coding

High capacity, secure audio watermarking technique integrating spread spectrum and linear predictive coding

An efficient watermarking algorithm for digital audio data in security applications

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

Robust audio watermarking algorithm resisting cropping based on SIFT transform

Robust audio watermarking algorithm resisting cropping based on SIFT transform

Robust Watermarking Using FFT and Cordic QR Techniques

Digital media sharing and access in today’s world of the internet is very frequent for every user. The management of digital rights may come into threat easily as the accessibility of data through the internet become wide. Sharing digital information under security procedures can be easily compromised due to the various vulnerabilities floating over the internet. Existing research has been tied to protecting internet channels to ensure the safety of digital data. Researchers have investigated various encryption techniques to prevent digital rights management but certain challenges including external potential attacks cannot be avoided that may give unauthorized access to digital media. The proposed model endorsed the concept of watermarking in digital data to uplift media security and ensure digital rights management. The system provides an efficient procedure to conduct over-watermarking in digital audio signals and confirm the avoidance of ownership of the host data. The proposed technique uses a watermark picture as a signature that has been initially encrypted with Arnold's cat map and cyclic encoding before being embedded. The upper triangular R-matrix component of the energy band was then created by using the Fast Fourier transform and Cordic QR procedures to the host audio stream. Using PN random sequences, the encrypted watermarking image has been embedded in the host audio component of the R-matrix. The same procedure has been applied to extract the watermark image from the watermarked audio. The proposed model evaluates the quality of the watermarked audio and extracted watermark image. The average PSNR of the watermarked audio is found to be 37.01 dB. It has also been seen that the average PSNR, Normal cross-correlation, BER, SSMI (structure similarity index matric) value for the extracted watermark image is found to be 96.30 dB, 0.9042 units, 0.1033 units, and 0.9836 units respectively. Further, the model has been tested using various attacks to check its robustness. After applying attacks such as noising, filtering, cropping, and resampling on the watermarked audio, the watermark image has been extricated and its quality has been checked under the standard parameters. It has been found that the quality of the recovered watermark image satisfying enough to justify the digital ownership of the host audio. Hence, the proposed watermarking model attains a perfect balance between imperceptibility, payload, and robustness.

Hiding Full-Color Images into Audio with Visual Enhancement via Residual Networks

Watermarking is a viable approach for safeguarding the proprietary rights of digital media. This study introduces an innovative fast Fourier transform (FFT)-based phase modulation (PM) scheme that facilitates efficient and effective blind audio watermarking at a remarkable rate of 508.85 numeric values per second while still retaining the original quality. Such a payload capacity makes it possible to embed a full-color image of 64 × 64 pixels within an audio signal of just 24.15 s. To bolster the security of watermark images, we have also implemented the Arnold transform in conjunction with chaotic encryption. Our comprehensive analysis and evaluation confirm that the proposed FFT–PM scheme exhibits exceptional imperceptibility, rendering the hidden watermark virtually undetectable. Additionally, the FFT–PM scheme shows impressive robustness against common signal-processing attacks. To further enhance the visual rendition of the recovered color watermarks, we propose using residual neural networks to perform image denoising and super-resolution reconstruction after retrieving the watermarks. The utilization of the residual networks contributes to noticeable improvements in perceptual quality, resulting in higher levels of zero-normalized cross-correlation in cases where the watermarks are severely damaged.

An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods. The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform (DWT) and the optimized deep learning technique. The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network (DCNN). The hyperparameter tuning is performed by the so-called search location optimization, which minimizes the errors in the classifier. The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate (BER), Mean Square Error (MSE), and Signal-to-noise ratio. The BER, MSE, and SNR of the proposed audio watermarking model without the noise are 0.082, 0.099, and 45.363 respectively, which is found to be better performance than the existing watermarking models.

Retracted: A New Blind IoT-Based MP3 Audio Watermarking Scheme for Content Integrity Checking and Copyright Protection

Retracted: A New Blind IoT-Based MP3 Audio Watermarking Scheme for Content Integrity Checking and Copyright Protection

An Effective Data-driven Audio Watermarking Technique

Abstract: Audio piracy remains a persistent issue, highlighting the crucial need to protect audio signals and respect intellectual property rights. To tackle this challenge, we propose a two-step procedure consisting of intrinsic time scale decomposition (ITD) and Singular Value Decomposition (SVD).In our approach, the audio to be watermarked is initially divided into intrinsic mode functions and a low-frequency residue component using an iterative time scale decomposition technique. Subsequently, we apply Singular Value Decomposition to both the low-frequency component and the watermark. This allows us to embed the watermark within the residue component of the audio.To generate the watermarked audio, we combine the intrinsic mode functions with the watermarked residue component using inverse Intrinsic Time Scale Decomposition. This ensures that the watermark becomes integrated into the audio signal. Notably, our proposed approach effectively addresses computational complexity, a common challenge, while also prioritizing robustness and imperceptibility in line with advanced methods employed in the field.

A New Hybrid Method for Secure Data Transmission Using Watermarking based on Fuzzy Encryption in IoT

Irregular fuzzy mapping is used for encryption and scrambling of image information in this paper. The proposed image encryption algorithm has many characteristics including large key space, few relationships between the pixels of the encrypted image, high sensitivity to the key and high security, which can effectively protect the security of the encrypted image. Therefore, a robust audio watermarking method that hides encrypted image information in the time domain is presented. After dividing the audio signal into consecutive parts, five parameters are calculated independently, including the average frequency, edge, energy, zero crossing rate, and standard deviation for different parts of the signal. With the LBP method, the encoded image is placed in different parts based on the weights determined by the fuzzy system defined on the five parameters for each part of the audio signal. Consequently, the distortion caused by hiding the watermark will not result in adverse listening effects, and the watermark will remain resistant to attacks and common audio processing. Based on the simulation results of the proposed technique under MATLAB software, it reveals the resistance of the watermark against various attacks and increasing the speed of calculations. Due to the combination of watermarking and simultaneous encoding of image information, as well as examining a large number of image evaluation criteria, including information entropy criteria and peak signal-to-noise ratio (PSNR), number of pixel change rate (NPCR), unified average changing intensity (UACI), we have been able to create a good improvement for the security of information transmission in the IoMT field.

Research on Copyright Protection Technology based on MIDI Music Structural Features

<p>Due to the extensive use of Musical Instrument Digital Interface (MIDI) music in education, entertainment and business, copyright infringements of MIDI music are increasing rapidly. The low robustness of existing MIDI music watermarking is the main problem. Therefore, this paper proposes two novel MIDI music watermarking algorithms to comprehensively protect the copyright of MIDI music. We first propose a dual threshold audio watermarking algorithm based on MIDI note time difference and velocity value. It extracts a main melody audio track of MIDI music through a Ho-Kashyap (H-K) algorithm, generates an adaptive em-bedding double threshold for the main melody audio track, and uses a time fluctuation algorithm to embed the watermark information. We also propose a MIDI music watermarking algorithm in frequency domain using Discrete Cosine Transform (DCT). It converts the pitch of MIDI music to the fundamental frequency, obtains the Direct Current (DC) component of the fundamental frequency by DCT transform, combines the watermark information and the DC component to obtain the secret pitch, and takes the difference between the secret pitch and the original pitch as the new watermark information. Finally, a general attack experiment was conducted on the two watermarking algorithms to verify the feasibility and security. In this paper, the mean square error method and the normalized correlation coefficient method are selected to evaluate the quality of MIDI music objectively. An absolute information entropy evaluation model is proposed to evaluate MIDI music quality. Experimental results show that the proposed algorithms can extract effective watermark after MIDI command attacks with a MIDI music cutting rate of more than 88% and tampering rate of less than 5%, which means that the proposed algorithms are robust against attacks.</p> <p> </p>

Limit Bipolar Sequences for Patchwork-Based Robust Digital Audio Watermarking

Ensuring the robustness of digital audio watermarking under the influence of interference, various transformations and possible attacks is an urgent problem. One of the most used and fairly stable marking methods is the patchwork method. Its robustness is ensured by the use of expanding bipolar numerical sequences in the formation and embedding of a watermark in a digital audio and correlation detection in the detection and extraction of a watermark. An analysis of the patchwork method showed that the absolute values of the ratio of the maximum of the autocorrelation function (ACF) to its minimum for expanding bipolar sequences and extended marker sequences used in traditional digital watermarking approach 2 with high accuracy. This made it possible to formulate criteria for searching for special expanding bipolar sequences, which have improved correlation properties and greater robustness. The article developed a mathematical apparatus for searching and constructing limit-expanding bipolar sequences used in solving the problem of robust digital audio watermarking using the patchwork method. Limit bipolar sequences are defined as sequences whose autocorrelation functions have the maximum possible ratios of maximum to minimum in absolute value. Theorems and corollaries from them are formulated and proved: on the existence of an upper bound on the minimum values of autocorrelation functions of limit bipolar sequences and on the values of the first and second petals of the ACF. On this basis, a rigorous mathematical definition of limit bipolar sequences is given. A method for searching for the complete set of limit bipolar sequences based on rational search and a method for constructing limit bipolar sequences of arbitrary length using generating functions are developed. The results of the computer simulation of the assessment of the values of the absolute value of the ratio of the maximum to the minimum of the autocorrelation and cross-correlation functions of the studied bipolar sequences for blind reception are presented. It is shown that the proposed limit bipolar sequences are characterized by better correlation properties in comparison with the traditionally used bipolar sequences and are more robust.