Data hiding within audio signals

Petrovic, R.; Winograd, J.M.; Jemili, Kanaan; Metois, E.

doi:10.1109/telsks.1999.804701

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…This means that, if coding space is a linear projection of pixel space, image watermarks will not be robust to changes in contrast. Some non-linear coding spaces have been proposed [16], [3], but few experimental results with these spaces have been reported.…”

Section: B Practical Dirty-paper Codesmentioning

confidence: 99%

Applying Informed Coding and Embedding to Design a Robust High-Capacity Watermark

Miller

Doerr

Cox

2004

IEEE Trans. on Image Process.

210

187

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We describe a new watermarking system based on the principles of informed coding and informed embedding. This system is capable of embedding 1380 bits of information in images with dimensions 240 × 368 pixels. Experiments on 2000 images indicate the watermarks are robust to significant valumetric distortions, including additive noise, low pass filtering, changes in contrast, and lossy compression.Our system encodes watermark messages with a modified trellis code in which a given message may be represented by a variety of different signals, with the embedded signal selected according to the cover image. The signal is embedded by an iterative method that seeks to ensure the message will not be confused with other messages, even after addition of noise. Fidelity is improved by the incorporation of perceptual shaping into the embedding process. We show that each of these three components improves performance substantially.

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Section: B Practical Dirty-paper Codesmentioning

confidence: 99%

Applying Informed Coding and Embedding to Design a Robust High-Capacity Watermark

Miller

Doerr

Cox

2004

IEEE Trans. on Image Process.

210

187

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show abstract

“…Over the past few years, most proposed audio watermarking algorithms [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] can be grouped into two categories: Spread spectrum (SS) modulation [8][9][10] and quantization index modulation (QIM) [11][12][13][14]. All these methods utilize either a time domain [15,16] or a transform domain such as fast Fourier transform [17], discrete cosine transform [18], or discrete wavelet transform [19].…”

Section: Audio Watermarking Algorithmsmentioning

confidence: 99%

Autocorrelation Modulation-Based Audio Blind Watermarking Robust Against High Efficiency Advanced Audio Coding

Hong

Kim

2019

Applied Sciences

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High Efficiency Advanced Audio Coding (HE-AAC) is a lossy compression method for digital audio data which supplies high-quality audio at a very low bit rate. In this paper, the audio blind watermarking algorithm, on the basis of autocorrelation modulation, is introduced to maximize the robustness against low-bit rate HE-AAC. The watermark is embedded by modulating the normalized correlation of the original signal as well as its delayed version. The signal-to-noise ratio of before and after HE-AAC compression decides the strength of the embedding watermark. The watermarking embedding strength is guaranteed by the feedback process. The effectiveness of the proposed method is proven using the Perceptual Evaluation of Audio Quality algorithm and bit error rate of recovered watermarks under HE-AAC compression on mono, stereo and 5.1 channel audio. Experimental results show that the proposed method provides good performance in terms of imperceptibility, robustness and data payload compared with some recent state-of-the-art watermarking methods under an MPEG-2 Audio Layer III (MP3) compression attack.

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“…Therefore, EE is calculated by (8). Because the bits of S is overwritten on the location of least significant bit of C , EE is calculated by amplitudes is equivalent to computing EE by bit (9).…”

Section: -Embedding Error (Ee): According To [1] [4] Ee Is Calculatedmentioning

confidence: 99%

Embedding Data into Audio Signal by Combining Sliding Window Technique with a Novel Marking Bit Method

Tam

Tran

Thuy

et al. 2015

Lecture Notes in Electrical Engineering

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Abstract. Embedding data into audio signal is an interesting field and attracted by many scientists. It allows taking advantage of available transmission lines to transmit data with safety and high security. However, the biggest challenge while embedding data in the audio signal is to achieve a small embedding error (EE) and large signal-to-noise ratio (SNR). If EE is large and SNR is small, it is vulnerable to be tracked and attacked by enemies during transmitting embedded audio signals. In this paper, we have proposed to combine the sliding window technique with a novel marking bit method. The sliding window technique is used to find out the bit segment of the original audio signal coinciding with the bit segment of data needed to be embedded. After that, we mark the location of windows found by the coded bit, and overwrite the coded bit on the original audio signal at the appropriate locations. The experimental results show that the proposed method provides a better quality than using other embedded methods such as the LSB (Least Significant Bit), ELS (Embedding Large Sample) and without using sliding windows.

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Data hiding within audio signals

Cited by 14 publications

References 21 publications

Applying Informed Coding and Embedding to Design a Robust High-Capacity Watermark

Applying Informed Coding and Embedding to Design a Robust High-Capacity Watermark

Autocorrelation Modulation-Based Audio Blind Watermarking Robust Against High Efficiency Advanced Audio Coding

Embedding Data into Audio Signal by Combining Sliding Window Technique with a Novel Marking Bit Method

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