A New Adaptive Digital Audio Watermarking Based on Support Vector Regression

A Survey: Digital Audio Watermark Designed Methods

“…Furthermore, this calculation proposed additionally such merits, for example, simple computation, simple implementation; all these merits enhance the practicality and application value for audio copyright protection. By using this method tradeoff between robustness, imperceptibility and security can't be satisfied [3]. Fabrizio Guerrini, Masahiro et.al [4] in this paper, they exhibited an algorithm for a HDR detectable watermarking system framework with the prerequisites of intangibility and vigor i.e.…”

“…Its computation make process complex and it is limited only for sound flag [2]. X. Wang, W. Qi, and et.al [3]. The new adaptive blind digital audio watermarking algorithm is proposed in this paper.…”

Overview of Audio Watermarking Algorithm against Synchronization Attacks

“…In [14], 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.…”

Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

“…The payload capacity must be large enough to accommodate necessary information. Different methods were attempted on various domains, such as time [1][2][3][4][5], Fourier transform [6][7][8], cepstral transform [9][10][11][12][13], discrete cosine transform (DCT) [14][15][16][17], and discrete wavelet transform (DWT) [14,16,[18][19][20][21][22][23].…”

“…One way to enhance the embedding efficiency is to exploit the auditory characteristics so that the embedding strength is sufficiently high to withstand attacks without introducing audible distortion. The methods presented in [16,17,22] demonstrated the benefit of exploiting the signal characteristics, but they relied on heuristic rules to decide the embedding strength. In these methods, even though some attention was paid to adjust relevant parameters to reach optimal performance, the connection between multiple transform domains and human auditory properties has not been thoroughly addressed.…”