Tamper recovery algorithm for digital speech signal based on DWT and DCT

Abstract

It is a challenging work to design tamper recovery schemes for digital speech signal. Briefly, there are two problems need to be solved. One is that the signals used to tamper recovery are difficult to generate and embed, and the second is that it's hard to tamper location precisely for attacked speech signal. In this paper, compression and reconstruction method based on discrete wavelet transform (DWT) and discrete cosine transform (DCT) is given, to obtain the compressed signals used to tamper recovery. And then frame number and compressed signals are embedded based on block-based method. Attacked signal can be located by frame number, and compressed signals are extracted and used to reconstruct the attacked signal. Theory analysis and experimental results indicate that the scheme proposed not only improves the accuracy of tamper localization, but also can reconstruct the attacked signals.

Self-recovery scheme for audio restoration using auditory masking

“…The schemes [21, 22], and [23] are compared with our proposed scheme. In order to present a quantitative comparison for standardized transparency evaluation, implementations of all the schemes are needed, which is future research.…”

“…Table 6 presents a comparison of the most significant properties of the schemes reported by the authors. As can be seen, the schemes of [21] and [22] present good transparency of their watermarked and restored signals. However, since they use a lossy compressed version of the signals to construct the information for self-recovery, these schemes cannot achieve perfect restoration.…”

“…Self-recovery schemes for speech, such as in [21, 22], have been proposed and can obtain an approximate restoration of the speech signals. A self-recovery scheme for audio signals that reports perfect restoration has been proposed by [23]; however, the results reported are inconclusive.…”

“…The scheme proposed by [22] is a self-recovery scheme for speech signals. It obtains a compressed version of the speech by calculating a 3-level DWT and a DCT; the coefficients from both transforms are concatenated to form the compressed signal.…”

Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain

“…All of these algorithms are designed in a single-transform domain, and there are many schemes designed in hybrid domains in recent years. Liu [24] proposed a scheme for audio signal tamper recovery and location tampering based on DWT and DCT. Hu [25] proposed an audio watermarking algorithm to achieve invisible data hiding based on DWPT, SVD and quantization index modulation (QIM) hybrid domains.…”