DeAR：一种基于深度学习的音频重录音鲁棒水印
DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking
AI总结：
针对音频重录音攻击导致水印失效的问题，提出基于深度学习的抗重录音音频水印框架DeAR，通过设计模拟重录音失真的专用层，在高质量嵌入下实现了98.55%的比特恢复准确率。
摘要：
音频水印被广泛用于泄露源追踪。水印的鲁棒性决定了算法的可追踪性。随着数字技术的发展，音频重录音（AR）已成为窃取机密的高效且隐蔽的手段。AR过程会在保留原始信息的同时，极大地破坏水印信号。这对当前阶段的音频水印提出了新要求，即对AR失真具有鲁棒性。遗憾的是，由于AR过程的复杂性，现有算法均无法有效抵抗AR攻击。为解决这一局限，本文提出DeAR，一种基于深度学习的抗音频重录音水印。受基于DNN的图像水印启发，我们率先为音频载体开创了深度学习框架，基于该框架可有效嵌入和提取水印信号。同时，为抵抗AR攻击，我们细致分析了AR过程中发生的失真，并设计了相应的失真层以配合所提出的水印框架。大量实验表明，所提算法不仅能抵抗常见的电子信道失真，还能抵抗AR失真。在高质量嵌入（SNR=25.86dB）的前提下，在常见重录音距离（20cm）的情况下，该算法能有效实现98.55%的平均比特恢复准确率。
英文摘要：
Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
