Objective

2

Under the background of the continuously increasing quantity of digital documents transmitted over the Internet

efficient and practical data hiding techniques should be designed to protect intellectual property rights. Digital watermarking techniques have been historically used to ensure security in terms of ownership protection and tamper-proofing for various data formats

including images

audio

video

natural language processing software

and relational databases. This study focuses on audio watermarking. In general

digital audio watermarking refers to the technology of embedding useful data (watermark data) within a host audio without substantially degrading the perceptual quality of the host audio. For different purposes

audio watermarking can be divided into two classifications: robust and fragile audio watermarking. The former is used to protect ownership of digital audio. By contrast

the latter is used to authenticate digital audio

i.e.

to ensure the integrity of digital audio. A digital watermarking scheme generally has three major properties: imperceptibility

robustness

and payload. Imperceptibility indicates that the watermarked audio is perceptually indistinguishable from the original one. This property is required to maintain the commercial value of audio data or the secrecy of embedded data. Robustness refers to the ability of a watermark to survive various attacks

such as JPEG/MP3 compression

additive noise

filtering

and amplification. Payload refers to the total amount of information that can be hidden within digital audio. Imperceptibility

robustness

and payload are three major requirements of any digital audio watermarking system to guarantee desired functionalities. However

a trade-off exists among them from the information-theoretic perspective. Simultaneously improving robustness

imperceptibility

and payload has been a challenge for digital audio watermarking algorithms. A digital audio watermarking scheme must be robust against various possible attacks. Attacks that attempt to destroy or invalidate watermarks can be classified into two types: noise-like common signal processing operations and desynchronization attacks. Desynchronization attacks are more difficult to address than other types of attacks. Designing a robust digital audio watermarking algorithm against desynchronization attacks is a challenging task.

Method

2

In this study

we propose a new second-generation digital audio watermarking in the undecimated discrete wavelet transform (UDWT) domain based on robust local audio features. First

robust audio feature points are detected by utilizing a smooth gradient. These feature points are always invariant to common signal processing operations and desynchronization attacks. Then

local digital audio segments

centering at the detected audio feature points

are extracted for watermarking use. Lastly

a watermark is embedded into local digital audio segments in the UDWT domain by modulating low-frequency coefficients. We use robust significant UDWT coefficients that can effectively capture important audio texture features to accurately locate watermark embedding/extraction position

even when under desynchronization attacks.

Result

2

To evaluate the performance of our scheme

watermark imperceptibility and robustness tests are conducted for the proposed watermarking algorithm. The watermark detection results of the proposed algorithm are compared with those of several state-of-the-art audio watermarking schemes against various attacks under equal conditions. All the audio signals in the test are music with 16 bit/sample

44.1 kHz sample rates

and 15 s duration. All our experiments are executed on a personal computer with Intel Core i7-4790 CPU 3.60 GHz

16 GB memory

and Microsoft Windows 7 Ultimate operating system. Moreover

MATLAB R2016a is used to perform the simulation experiments. To quantitatively evaluate the imperceptibility performance of the proposed watermarking algorithm

we also calculate signal-to-noise ratio (SNR)

which is an objective criterion and always used to evaluate audio quality. The SNR of the proposed scheme is improved by 5.7 dB on average

demonstrating its effectiveness in terms of the invisibility of the watermark. Watermark robustness is measured as the correctly extracted percentage of extracted segments. The average detection rate remains at 0.925 and 0.913

which are higher than those of most traditional algorithms. Therefore

the experimental results show that the proposed approach exhibits good transparency and strong robustness against common audio processing activities

such as MP3 compression

resampling

and requantization. The scheme also demonstrates good robustness against desynchronization attacks

such as random cropping

pitch-scale modification

and jittering.

Conclusion

2

An audio watermarking algorithm based on robust feature points of the wavelet domain is proposed on the basis of audio content features and the stability of the low-frequency coefficient of UDWT. First

the original audio is dealt with using UDWT

and then by calculating the first-order gradient responses of the low-frequency coefficient

ranking these responses in descending order

and selecting the highest response as criterion to set the threshold. From these processes

stable and evenly distributed feature points are obtained. Then

the robust feature point is set for identification

and audio watermarking is embedded. Finally

the low-frequency coefficient is inserted into watermarking via quantization index modulation. The proposed scheme effectively solves the disadvantages of poor stability and uneven distribution of audio feature points

improving the resistance of digital audio watermarks to pitch-scale modification

random cropping

and jittering attacks.