Tatouage audio par EMD

In this paper a new adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark and the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving audio perceptual quality of the host signal. The data embedding rate of the proposed algorithm is 46.9–50.3 b/s. Relying on exhaustive simulations, we show the robustness of the hidden watermark for additive noise, MP3 compression, re-quantization, filtering, cropping and resampling. The comparison analysis shows that our method has better performance than watermarking schemes reported recently

EFFECTIVE ROBUST PATCHWORK METHOD TO THE VULNERABLE ATTACK FOR DIGITAL AUDIO WATERMARKING

This paper presents patchwork based digital audio watermarking. The advanced growth in transmission of digital data has resulted in a corresponding elevation in the need for copyright protection of signal. Cryptography and steganography are used for the content protection but do not completely solve the copyright issue. Watermarking is a method to protect and identify the digital data while maintaining the quality of the host media, it permits various types of watermarks to be hidden in audio signal e.g. image, audio and video. This paper limits on image embedding technique using patchwork-based method. In patchwork based method average of all segments of approximate coefficients is calculated for embedding watermark into sound signal. The experimental results shows that proposed method achieves imperceptibility for audio signal as watermarked audio signal is inaudible after embedding watermark and robustness of watermark against different signal processing attacks with higher PSNR. The resulting audio is robust to attacks and exhibits good quality in term of peak signal to noise ratio. The simulation results show the effectiveness of the proposed system

High capacity data embedding schemes for digital media

High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen

Audio watermarking using transformation techniques

Watermarking is a technique, which is used in protecting digital information like images, videos and audio as it provides copyrights and ownership. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. This thesis attempts to solve the quantization based audio watermarking technique based on both the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The underlying system involves the statistical characteristics of the signal. This study considers different wavelet filters and quantization techniques. A comparison is performed on diverge algorithms and audio signals to help examine the performance of the proposed method. The embedded watermark is a binary image and different encryption techniques such as Arnold Transform and Linear Feedback Shift Register (LFSR) are considered. The watermark is distributed uniformly in the areas of low frequencies i.e., high energy, which increases the robustness of the watermark. Further, spreading of watermark throughout the audio signal makes the technique robust against desynchronized attacks. Experimental results show that the signals generated by the proposed algorithm are inaudible and robust against signal processing techniques such as quantization, compression and resampling. We use Matlab (version 2009b) to implement the algorithms discussed in this thesis. Audio transformation techniques for compression in Linux (Ubuntu 9.10) are applied on the signal to simulate the attacks such as re-sampling, re-quantization, and mp3 compression; whereas, Matlab program for de-synchronized attacks like jittering and cropping. We envision that the proposed algorithm may work as a tool for securing intellectual properties of the musicians and audio distribution companies because of its high robustness and imperceptibility

DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.Comment: Accepted by AAAI202

New Digital Audio Watermarking Algorithms for Copyright Protection

This thesis investigates the development of digital audio watermarking in addressing issues such as copyright protection. Over the past two decades, many digital watermarking algorithms have been developed, each with its own advantages and disadvantages. The main aim of this thesis was to develop a new watermarking algorithm within an existing Fast Fourier Transform framework. This resulted in the development of a Complex Spectrum Phase Evolution based watermarking algorithm. In this new implementation, the embedding positions were generated dynamically thereby rendering it more difficult for an attacker to remove, and watermark information was embedded by manipulation of the spectral components in the time domain thereby reducing any audible distortion. Further improvements were attained when the embedding criteria was based on bin location comparison instead of magnitude, thereby rendering it more robust against those attacks that interfere with the spectral magnitudes. However, it was discovered that this new audio watermarking algorithm has some disadvantages such as a relatively low capacity and a non-consistent robustness for different audio files. Therefore, a further aim of this thesis was to improve the algorithm from a different perspective. Improvements were investigated using an Singular Value Decomposition framework wherein a novel observation was discovered. Furthermore, a psychoacoustic model was incorporated to suppress any audible distortion. This resulted in a watermarking algorithm which achieved a higher capacity and a more consistent robustness. The overall result was that two new digital audio watermarking algorithms were developed which were complementary in their performance thereby opening more opportunities for further research

Spread Spectrum Based High Embedding Capacity Watermarking Method for Audio Signals

Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking methods have been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples