Research And Improvements On Spread-Spectrum-Based Audio Watermarking In Time Domain

Recent developments on networks and multimedia compression techniques allow digital media to be copied, transmitted and edited conveniently. This makes enhancing the information security and preserving the intellectual property become urgent problems. One of the possible solutions is digital watermarking.Digital audio watermarking embeds additional information namely watermark such as copyright into the audio signal by making small modifications to the original data. This paper focuses on the audio watermarking for copyright protection which embeds the binary image data into the time domain of the audio signal based on the spread-spectrum technique. The main shortcomings of this watermarking are the low embedding rate and the lack of robustness to synchronization attacks (such as time-scale modification and pitch-scale modification). This paper presents some improvements in the detection (extraction) algorithm to increase the robustness without big altering to the embedding algorithm. The improvements involve: adaptive decision threshold, post-processing for resisting time-scale modification and whitening based on high pass filtering for resisting pitch-scale modification. The traditional detection techniques extract the information bits by comparing the correlation values against a fixed threshold, which is selected by experiments or experience. This paper proposes a scheme for adaptively selecting the threshold. By analyzing the distribution of the correlation values of all frames, the dividing point between the two distribution regions is chosen for the optimum threshold of this audio. The experiment results show that the optimum threshold varies with host audios and attacks, especially the latter. The adaptive threshold scheme not only makes the watermark more resistant to normal attacks, such as high order filtering, re-sampling, re-quantization and MPEG compression, but also makes it possibly robust against pitch-scale modification.Time-scale modification changes the duration of one audio signal by regularly duplicating or discarding small pieces of the original signal. Although the synchronization of watermark is damaged, the local data segments are not changed. So the correlation detection can still be carried out, except that the number of the extracted information bits is changed because of the changing of numbers of the audio samples, and the binary image represented by these information bits can not be identified because of warping. In this paper a simple and practical scheme called post-processing is propose to provide the resistance to time-scale modification. This scheme scales the extracted information bits to their original size by decimating and inserting. The scaling scale is the one corresponding to the minimum detection error found by exhaustively searching. The searching is implemented by a coarse searching and a fine searching to increase the efficiency. The experiment results show that this scheme has low requirement to computation and memory and is very efficient to time-scale modification. The detection error rate is lower than 16% when the time-scale modification scale is between 0.3 and 2.0.The pitch-scale modification is implemented by first time-scale modification and then re-sampling. Because of the equal spaced decimating and interpolation, there isn't any unchanged data segment. In theory, the correlation detection can't extract the watermark. But by a large number of experiments, we found that the optimum linear prediction error filter before the correlation is a non-linear phase high-pass filter and that the watermark under pitch-scale modification can be extracted successfully by substituting a linear phase high-pass finite impulse response filter for this optimum linear prediction error filter and by making use of the adaptive threshold. The experiment results show that this method is very efficient to pitch-scale modification. The detection error rate is lower than 15% when the pitch-scale modification scale is between 0.7 and 2.3. Considering that the high-pass filtering reduces the robustness to attacks like low-pass filtering, the final detector is the combination of the two detectors (adopting optimum prediction error filter and adopting linear phase high-pass filter respectively), that is to say, maximum of the two kinds of correlation values is taken for comparing. On the other hand, the robustness to other attacks is further improved by combing these two detection schemes.In short, the watermark detection algorithm proposed in this paper is very practical because of its robustness to almost all attacks and low computation complexity.