Adaptive watermarking algorithms for MP3 compressed audio signals
type: CreativeWork
description: MPEG-1 Layer 3, known as MP3, has generated a significant popularity for distributing digital music over the Internet. MP3 compresses digital music with high ratio while keeping high sound quality. However, copyright issue is raised because of illegal copy, redistribution and various malicious attacks. Digital watermarking is a technology that allows users to embed some imperceptible data into digital contents such as image, movie and audio data. Once a watermark is embedded into the original MP3 signal, it can be used to identify the copyright holder in order to prevent illegal copy and to verify the modification from the original content. This thesis presents two novel adaptive watermarking algorithms for MP3 compressed audio signals for copyright protection. Based on Human Auditory System, the proposed algorithms calculate the energy of the original audio signal and apply Gaussian analysis on MP3 frames to adaptively adjust the watermarking coefficients. Watermark is embedded adaptively and transparently during the MP3 compression. The first watermarking algorithm detects watermark based on Gaussian distribution analysis. To enhance the security of the watermark, the second watermarking algorithm embeds random watermark pattern and uses correlation coefficient to detect watermark. Both algorithms support blind watermark detection and perform well. The first algorithm is more robust while the second algorithm is more secure. LAME 3.96.2 open source was used as standard ISO MP3 encoder and decoder reference in this study. The experimental results show that the proposed watermarking algorithms can work on a variety of audio signals and survive most common signal manipulation and malicious attacks. As expected, the watermarking algorithms provide superior performance on MP3 compression.
abstract: MPEG-1 Layer 3, known as MP3, has generated a significant popularity for distributing digital music over the Internet. MP3 compresses digital music with high ratio while keeping high sound quality. However, copyright issue is raised because of illegal copy, redistribution and various malicious attacks. Digital watermarking is a technology that allows users to embed some imperceptible data into digital contents such as image, movie and audio data. Once a watermark is embedded into the original MP3 signal, it can be used to identify the copyright holder in order to prevent illegal copy and to verify the modification from the original content. This thesis presents two novel adaptive watermarking algorithms for MP3 compressed audio signals for copyright protection. Based on Human Auditory System, the proposed algorithms calculate the energy of the original audio signal and apply Gaussian analysis on MP3 frames to adaptively adjust the watermarking coefficients. Watermark is embedded adaptively and transparently during the MP3 compression. The first watermarking algorithm detects watermark based on Gaussian distribution analysis. To enhance the security of the watermark, the second watermarking algorithm embeds random watermark pattern and uses correlation coefficient to detect watermark. Both algorithms support blind watermark detection and perform well. The first algorithm is more robust while the second algorithm is more secure. LAME 3.96.2 open source was used as standard ISO MP3 encoder and decoder reference in this study. The experimental results show that the proposed watermarking algorithms can work on a variety of audio signals and survive most common signal manipulation and malicious attacks. As expected, the watermarking algorithms provide superior performance on MP3 compression.
author: Chen, Bingwei
language: en
keywords: Music., Computer Science.
identifier: Source: Masters Abstracts International, Volume: 48-01, page: 0446.
date published: 2013-11-07T19:03:05Z
date modified: 2026-07-24T03:39:42Z
provider: University of Ottawa
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Ottawa (Canada)

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10393/27963

PropertyValue
property ID: DOI
value: 10.20381/ruor-19005