A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

Abstract

Most existing extraction techniques in audio watermarking use conventional techniques in which some sets of special rules based on reverse embedding rules are used for watermark extraction and have many weaknesses, like very low robustness to destructive attacks. To overcome this problem, the use of machine learning-based methods has increased in recent years in this field. The disadvantage of these methods is the high reliance on a unique classifier and lack of proper efficiency when achieving high capacity, which is a major challenge in audio watermarking. The main purpose of this paper is to present a method that covers the weak points of conventional methods and simple intelligent methods and improves system performance using a synergistic combination of discrete wavelet transform (DWT) and ensemble-intelligent extraction approach by proposed combination of trained machine learning classifiers. For the embedding operation in the proposed method, the DWT and the difference in energy levels obtained through DWT coefficients are used. In the extraction section, three methods are used in parallel: (a) the trained support vector machine (SVM) classifier with RBF kernel, (b) trained SVM classifier with quadratic kernel and (c) the trained K-nearest neighbor classifier; finally, the majority function is used to vote and make a final decision to create an intelligent-based watermark detector. A training set is required to train the classifiers, whose bit sequence is generated by a proposed 5-bit linear-feedback shift register. The results of various experiments indicate that this ensemble method has achieved the appropriate imperceptibility and high capacity, along with higher robustness compared to conventional techniques and individual learning classifiers.

Robust image watermarking in DCT domain using optimal inter‐block difference

“…Standard metrics to evaluate the robustness of a watermarking scheme include the bit error rate (BER) and the normalized correlation parameters. While the first is used to measure the fidelity of the extracted watermark image to the original watermark image (17), the latter represents the ratio of erroneous extracted bits to the total of embedded bits (18).…”

“…Figures [15][16][17] show the robustness results in terms of BER(%) after various noise contamination attacks. For GN attacks (Figure 15), the Man image which presents the highest PSNR (71 dB) among the tree tested images shows the best robustness, where the BER is always below 1.5% for different noise variances between 0.001 and 0.1.…”

“…The most used transform domains include the discrete wavelet transform (DWT) [17][18][19][20][21] and the discrete cosine transform (DCT). [22][23][24][25] The DWT has been widely used for watermarking because of its excellent spatial localization performance and multi-resolution analysis of signals.…”

Zero Watermarking for the TIN DEM Data Based on the Edge Length

“…In other words, there are multiple watermark bits on one watermark index. Thus, the majority voting mechanism [33,34] will be introduced to figure this out. For a watermark index, if the number of zeros in its watermark bit is more than that of ones, then its watermark bit is 0, otherwise 1.…”

Audio and Video Matching Zero‐Watermarking Algorithm Based on NSCT

“…In order to improve the robustness of watermarking algorithm, more scholars begin to pay attention to the research of watermarking algorithm in transform domain and transfer the embedding position of watermark from time domain to transform domain. For example, [16] proposed the audio watermarking technology based on DWT, [17] proposed the audio watermarking technology based on SVD (singular value decomposition) and fractional Fourier transform, and [18] proposed the audio watermarking technology based on DWT and SVD. At present, most of the watermarking algorithms of audio and video are designed separately, but multimedia data is composed of audio and video together, so it is not enough to protect only one of them.…”