title: IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding
summary: The audio watermarking technique embeds messages into audio and accurately
  extracts messages from the watermarked audio. Traditional methods develop
  algorithms based on expert experience to embed watermarks into the time-domain
  or transform-domain of signals. With the development of deep neural networks,
  deep learning-based neural audio watermarking has emerged. Compared to
  traditional algorithms, neural audio watermarking achieves better robustness by
  considering various attacks during training. However, current neural
  watermarking methods suffer from low capacity and unsatisfactory
  imperceptibility. Additionally, the issue of watermark locating, which is
  extremely important and even more pronounced in neural audio watermarking, has
  not been adequately studied. In this paper, we design a dual-embedding
  watermarking model for efficient locating. We also consider the impact of the
  attack layer on the invertible neural network in robustness training, improving
  the model to enhance both its reasonableness and stability. Experiments show
  that the proposed model, IDEAW, can withstand various attacks with higher
  capacity and more efficient locating ability compared to existing methods.
paper_authors._id: 66fb94e5524b58795e7adc28
paper_authors.name: Pengcheng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-01T09:13:43.053Z
paper_authors._id: 66fbba9052eeec12b1915e7d
paper_authors.avatarUrl: /avatars/317fcc57290686094050bf97fdcc6b49.svg
paper_authors.fullname: Pechola Lee
paper_authors.type: user
paper_authors.user: Pechola
paper_authors._id: 66fb94e5524b58795e7adc29
paper_authors.name: Xulong Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-01T06:25:15.435Z
paper_authors._id: 64631720d9b490218812ef63
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/6qXagHvh58pQbq9RK9klE.png
paper_authors.fullname: Xulong Zhang
paper_authors.type: user
paper_authors.user: zhangxulong
paper_authors._id: 66fb94e5524b58795e7adc2a
paper_authors.name: Jing Xiao
paper_authors._id: 66fb94e5524b58795e7adc2b
paper_authors.name: Jianzong Wang
paper_title: IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding
paper_summary: The audio watermarking technique embeds messages into audio and accurately
  extracts messages from the watermarked audio. Traditional methods develop
  algorithms based on expert experience to embed watermarks into the time-domain
  or transform-domain of signals. With the development of deep neural networks,
  deep learning-based neural audio watermarking has emerged. Compared to
  traditional algorithms, neural audio watermarking achieves better robustness by
  considering various attacks during training. However, current neural
  watermarking methods suffer from low capacity and unsatisfactory
  imperceptibility. Additionally, the issue of watermark locating, which is
  extremely important and even more pronounced in neural audio watermarking, has
  not been adequately studied. In this paper, we design a dual-embedding
  watermarking model for efficient locating. We also consider the impact of the
  attack layer on the invertible neural network in robustness training, improving
  the model to enhance both its reasonableness and stability. Experiments show
  that the proposed model, IDEAW, can withstand various attacks with higher
  capacity and more efficient locating ability compared to existing methods.
paper_ai_keywords: deep neural networks neural audio watermarking robustness attacks dual-embedding watermarking model invertible neural network