Overview of Audio Watermarking Algorithm against Synchronization Attacks <s> E. Implicit Synchronization Scheme <s> In this paper, a blind digital audio watermarking scheme is proposed. A psychoacoustic model is used to ensure the imperceptibility of the embedded watermark. The watermark is embedded in the discrete cosine transform (DCT) domain by changing the phase of five information carriers determined using the psychoacoustic model. Blind recovery is achieved through the application of a novel synchronization scheme, the peak point extraction (PPE) scheme. Error correcting codes are used to enhance the robustness of the system. Informal listening reveals excellent imperceptibility of the embedded watermark. The watermarked signal was put through various forms of attacks. Experimental results show that recovery of watermark is perfect for signals not subjected to attack and the system is robust against attacks such as cropping and re-sampling. <s> BIB001 </s> Overview of Audio Watermarking Algorithm against Synchronization Attacks <s> E. Implicit Synchronization Scheme <s> A new framework for data embedding in audio is proposed. The basic idea of the algorithm is to change the length of the intervals between salient points of the audio signal to embed data. The intervals are quantized and the data is embedded in the quantization indices. In our particular implementation, we use the wavelet extrema of the signal envelope as the salient points. We propose novel ideas for practical implementation that can be used by other data embedding schemes as well. The algorithm is robust to common audio processing operations, e.g., mp3 lossy compression, low pass filtering, sampling rate conversion, and time-scale modification (TSM). The perceptual quality of the audio signal after embedding the data depends on the technique used for TSM. <s> BIB002 </s> Overview of Audio Watermarking Algorithm against Synchronization Attacks <s> E. Implicit Synchronization Scheme <s> A self-synchronization blind audio watermarking based on(t,n) threshold in wavelet transform is proposed in this paper.This technique can extract the watermark without using the original watermark and the host audio during the detection.It is a completely blind method during watermark detection.Watermark was divided into n shadows signal according to secret sharing scheme before the embedding process,and Shamir's(t,n) threshold scheme was used to distribute the watermark shadows to the users.Lagrange interpolation polynomial was used to realize it.Results show that t or more of those signal shadows can reconstruct the secret watermark,while t-1 or less signal shadows could not do it.The watermark used in this paper is meaningful speech and it was produced independent of the original audio.The watermark embedded intensity depended on the masking effect of human audio system.Audio processing such as time shift and time scaling modification can cause displacement between embedding and detection process and it is hence difficult for watermark to survive.The pitch detection is done using the coefficients of wavelet transform of the host audio,which is used as the synchronization point.Before watermark detection,the detector finds the synchronization point to realize the synchronization between the watermark embedding and detection process.Recently,blind source separation by independent component analysis(ICA) has received attention because of its potential applications in signal processing.ICA is adopted during the watermark detection to realize the true blind detection without any information about the host audio,watermark,embedding information and attacks.The accuracy of the watermarking extraction also depends on the statistical independence of original audio and watermark signal.To resist the cheating attacks,we use the one way hashing function before watermark reconstruction.Only t or more honest users can work collectively to reconstruct the secret,while dishonest users could not do it.The detailed performance analysis of the proposed watermarking method is presented.Different audio signals are used to test the robustness of the method.Experimental results show that the proposed audio watermarking technique is robust against many audio processing performed by popular watermark test software-Stirmark,such as low-pass filtering,median filtering,additive Gaussian noise,mp3 compression,time scaling,time shift,dithering and quantization. <s> BIB003 </s> Overview of Audio Watermarking Algorithm against Synchronization Attacks <s> E. Implicit Synchronization Scheme <s> A synchronization and a blind detect algorithm based on character of dual-channel audio was presented by calculating Mel frequency spectrum coefficient and using correlation between left and right channel,the synchronized segment was fix on and the performance was analyzed.After the watermarking was embedded into synchronized segment in both channels at the same time,it could be detected without original audio and watermarking information by calculat-ing the correlation of them between left and right channels.Experimental results show that the proposed approach can significantly improve the watermarking robustness under the attacks of adding noise,MP3 compression,resample and requantization. <s> BIB004 </s> Overview of Audio Watermarking Algorithm against Synchronization Attacks <s> E. Implicit Synchronization Scheme <s> On the basis of support vector regression (SVR), a new adaptive blind digital audio watermarking algorithm is proposed. This algorithm embeds the template information and watermark signal into the original audio by adaptive quantization according to the local audio correlation and human auditory masking. The procedure of watermark extraction is as follows. First, the corresponding features of template and watermark are extracted from the watermarked audio. Then, the corresponding feature of template is selected as training sample to train SVR and an SVR model is returned. Finally, the actual outputs are predicted according to the corresponding feature of watermark, and the digital watermark is recovered from the watermarked audio by using the well-trained SVR. Experimental results show that our audio watermarking scheme is not only inaudible, but also robust against various common signal processing (such as noise adding, resampling, requantization, and MP3 compression), and also has high practicability. In addition, the algorithm can extract the watermark without the help of the original digital audio signal, and the performance of it is better than other SVM audio watermarking schemes. <s> BIB005
Implicit synchronization is that the actual feature of carrier is used to identify the watermark embedding position, these feature points and the watermark information can be detected together. The type of algorithms depend on the invariance property of these features , and therefore the feature points may be higher regions of the audio signal energy or higher regions of zero-crossing rate, such as zero-crossing rate, pitch, tempo, frequency centroid. Implicit synchronization is the better strategy of resisting synchronization attacks, however, the scheme relies on the content of the audio signal, the audio signal which does not meet the requirements can not use this method. In BIB001 , the psychoacoustic model was used to ensure the imperceptibility of the embedded watermark. The watermark was hid in the Discrete Cosine Transform (DCT) domain by changing the phase of five information carriers determined using the psychoacoustic model. Blind detection was realized through the application of a novel synchronization scheme, the peak point extraction (PPE) scheme. Experimental results showed that the algorithm was robust against various attacks such as cropping and re-sampling In BIB002 , the paper presented an audio watermarking algorithm against synchronization attacks by changing the length of the intervals between salient points of the audio signal to embed data. The intervals were quantized and the watermark information was embedded in the quantization indices. The paper used the wavelet of the signal envelope as the salient point and proposed novel ideas for practical implementation that could be used by other data embedding schemes as well. The system was robust to mp3 compression, low pass filtering, sampling rate conversion, and time-scale modification (TSM). In BIB005 , according to support vector regression (SVR), the adaptive digital audio watermarking algorithm was presented which need not the original audio signal when the watermark was detected. This algorithm embedded the template information and watermark signal into the original audio by adaptive quantization according to the local audio correlation and human auditory masking. Experimental results showed that the audio watermarking scheme was not only imperceptible, but also robust against various common signal attacks and synchronization attacks. In BIB003 , a self-synchronization blind audio watermarking in DWT was presented based on (t, n) threshold. The scheme used the features of the audio signal transferred to embed the watermark information. In BIB004 , the authors presented the algorithm against synchronization attacks based on character of dual-channel audio which computed the Mel frequency spectrum coefficient and used the correlation between left and right channel. The watermark information was embedded into synchronized segment in left and right channel at the same time. The simulation results showed that the approach had good robustness. In  , the author took use of the audio cep-strum domain and energy features to decide the regions of embed frames. The simulation results proved the scheme is effective to resist common proceeding and synchronization attacks. In  , the paper proposed a digital audio watermarking algorithm which can resist the random cropping and TSM attacks based on the local statistical features of audio signal. Firstly, the approach divided the original audio into segments. Secondly, the histogram and the absolute mean value of each segment were computed to embed the watermark information. The experimental results proved the proposed algorithm could provide good performance for random cropping and TSM attacks.