Audio Watermarking Approach Research Articles

Ambiguity attacks on SVD audio watermarking approach using chaotic encrypted images

In this paper, we study the robustness of the proposed watermarking algorithm by Al-Nuaimy et al. (Digit Signal Process 21(6):764–779 2011) for audio signals which is based on singular value decomposition (SVD). It has been concluded that it is fundamentally flawed in its design, in that it falls to two ambiguity attacks where the extracted watermark is not the embedded one but determined by the reference watermark. In the first attack, when a watermarked audio signal is rewatermarked by an attacker’s watermark, this one can be easily extracted to claim ownership of the original audio signal. In the second attack, during the extracting process when an attacker uses the singular vectors of his watermark, he can extract the attacker’s watermark. Therefore, he can claim ownership of the watermarked audio signal. The experimental results prove that the proposed attacks create a false positive detection in watermark extraction. Therefore, Al-Nuaimy et al. algorithm cannot be used for security systems, data hiding and copyright protection.

Dual-Domain Audio Watermarking Algorithm Based on Flexible Segmentation and Adaptive Embedding

This paper proposes a novel dual-domain audio watermarking approach based on flexible segmentation and adaptive embedding aimed to improve robustness and imperceptibility. Compared with conventional watermarking strategies, the proposed approach has two advantages. First, a novel audio beat detection approach is designed to flexibly segment the audio, which provides stronger robustness to synchronization attacks. The audio is decomposed by the discrete wavelet packet transform. Then, the covariance relationships of the decomposition coefficients at different time instants are calculated to determine the locations of the beats and to establish a flexible segmentation model. Second, a dual-domain embedding approach is proposed to realize better robustness to compression attacks while maintaining imperceptibility. In each segment, the psychoacoustic model is used to calculate the audio masking threshold, which divides the signals into the masking signal domain and masked signal domain. The signals in the masking signal domain are robust to compression attacks, and the signals in the masked signal domain have better imperceptibility. To combine these advantages, we embed the watermark into the two domains simultaneously by using the distortion-compensated dither modulation quantization approach. To reduce the impact of the watermark on the original audio, the frequency band with the lowest mask-to-noise ratio is selected as the embedding position for each domain. Moreover, the adaptive quantization steps are calculated to control the embedding strength according to the masking effect. The adaptive embedding will improve the robustness to compression attacks without significantly affecting the original audio quality. The effectiveness of our approach is verified through simulation experiments.

SVD audio watermarking

It proposes an approach for audio watermarking using the singular value decomposition (SVD) mathematical technique. After transforming it into a 2-D format the encrypted image is embedded in the singular values of the audio signal. After watermark embedding, the audio signal is transformed again into a 1-D format. For encrypt the image chaotic encryption is used. It improves the quality of extracted images as proved experimentally, where it resists the noise and different attacks.

New secure and robust audio watermarking algorithm based on QR factorization in wavelet domain

Digital watermarking consists in embedding imperceptible information into a host signal. It has been proposed to solve problems as varied as the protection of the copyright, content authentication, fingerprinting and broadcast monitoring. This paper presents a new approach for audio watermarking using the QR factorization in wavelet domain. This approach is based on embedding a watermark binary image in the R matrices of low frequency blocks DWT coefficients of audio signal. In this algorithm, the watermark is embedded by applying a Quantization Index Modulation (QIM) process on the determined optimal sample for each matrix R. The watermark can be blindly extracted without the knowledge of the original audio signal. Experimental results show that the proposed audio watermarking scheme maintains high quality of the audio signal. Signal to Noise Ratio (SNR), Log Spectral Distortion (LSD) and Mean Opinion Score (MOS) are about 40 dB, 0.37 dB and 4.84, respectively. Moreover, the scheme is quite robust against common signal processing attacks such as noise addition, filtering and MP3 compression. In addition, this method ensures a secure extraction process by using a private key, making it suitable for secure applications such as copyright protection.

An SVD audio watermarking approach using chaotic encrypted images

This paper presents a new approach for audio watermarking using the Singular Value Decomposition (SVD) mathematical technique. The proposed approach can be used for data hiding in the audio signals transmitted over wireless networks and for multi-level security systems as will be stated in the applications section. This approach is based on embedding a chaotic encrypted watermark in the singular values of the audio signal after transforming it into a 2-D format. The selection of the chaotic encryption algorithm for watermark encryption is attributed to its permutation nature, which resists noise, filtering, and compression attacks. After watermark embedding, the audio signal is transformed again into a 1-D format. The transformation between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. The proposed approach can be implemented on the audio signal as a whole or on a segment-by-segment basis. The segment-by-segment implementation allows embedding the same watermark several times in the audio signal, which enhances the detectability of the watermark in the presence of severe attacks. Experimental results show that the proposed audio watermarking approach maintains the high quality of the audio signal and that the watermark extraction and decryption are possible even in the presence of attacks.

Segmenting Histogram-based Robust Audio Watermarking Approach

Based on the invariant features and data distribution in time domain before and after attacking on the audio, it is clearly that the standard variance shows good invariant statistical feature. The relation of four consecutive bins in a histogram maintains change less than 5%. Then, a general data selection approach, which meets the demands of normal distribution and other distributions, is created for the watermark hiding. Based on segmenting idea, an audio watermarking algorithm of embedding and extracting is designed with statistical features by the stable relation of four consecutive bins. Experimental results show that the algorithm can resist on attack of low-pass filtering with large hiding bandwidth up to 17 bit/s. It shows much better than the existed histogram approach with relations of three consecutive bins. The algorithm can also resist on the attacks of TSM and other common attacks.