哈希波 (HashWave):以区块链为动力的感知哈希,用于抵御分散网络中信号处理攻击的弹性音频盗版检测
Stuti Pandey1, Akhilendra Pratap Singh1, Dharmender Singh Kushwaha2
1Department of Computer Science and Engineering, NIT Meghalaya, Saitsohpen Sohra, 793108, Meghalaya, India.
Scientific reports
|November 17, 2025
概括
HashWave是一个新的区块链集成的音频指纹系统,可以检测音频盗版. 它对各种音频编辑提供了强大,可验证和高效的检测,优于传统方法.
科学领域:
- 计算机科学 计算机科学
- 数字信号处理 数字信号处理
- 密码学 密码学 密码学 密码学
背景情况:
- 分散的音频分发对盗版检测提出了挑战,因为传统方法缺乏稳定性和可验证性.
- 现有的解决方案,如数字权利管理 (DRM) 和传统的指纹 (例如,基于MFCC的哈希) 无法解决信号编辑或去中心化的需求.
- 深度学习嵌入式提供了稳定性,但需要大量的计算资源,阻碍了边缘和分散的应用程序.
研究的目的:
- 开发一种新的音频盗版检测框架,该框架对于去中心化环境具有编辑弹性,可验证和计算效率.
- 将强大的音频指纹技术与区块链技术集成为防改验证和分散存储.
- 创建一个实用且可扩展的解决方案,用于在各种数字生态系统中检测音频盗版.
主要方法:
- 提出HashWave,一个集成于区块链的感知哈希框架,结合了MFCC,chroma,CENS,CQT,光谱对比度和节奏/能量线索.
- 实现了操作意识加权和受约束的动态时间扭曲 (DTW),以处理时间尺度的音频编辑.
- 利用以太坊和IPFS进行分散的哈希存储,重复控制和可验证的作者身份.
主要成果:
- 在多个数据集和转换中,HashWave以0.957的曲线下面面积 (AUC) 和0.952的1%的假正率 (FPR) 的真正率 (TPR) 实现了高性能.
- 与仅使用MFCC的基线相比,该系统表现出更高的性能,并接近了深度学习嵌入式,CPU成本明显降低.
- 区块链集成提供了高效的去中心化存储和验证,平均上传和合同执行时间分别为0.017s和0.044s.
结论:
- 在分散的环境中,HashWave提供了一个实用,可扩展和安全的解决方案,用于在分散的环境中检测音频盗版.
- 该框架有效地解决了传统方法和计算密集型深度学习方法的局限性.
- HashWave适用于各种应用,包括流媒体,播客和Web3生态系统,确保内容完整性和可验证的所有权.
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