Invisible Watermarking for Audio Generation Diffusion Models

Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains. In the rapidly evolving field of audio-based machine learning, safeguarding model integrity and establishing data copyright are of paramount importance. This paper presents the first watermarking technique applied to audio diffusion models trained on mel-spectrograms. This offers a novel approach to the aforementioned challenges. Our model excels not only in benign audio generation, but also incorporates an invisible watermarking trigger mechanism for model verification. This watermark trigger serves as a protective layer, enabling the identification of model ownership and ensuring its integrity. Through extensive experiments, we demonstrate that invisible watermark triggers can effectively protect against unauthorized modifications while maintaining high utility in benign audio generation tasks.Comment: This is an invited paper for IEEE TPS, part of the IEEE CIC/CogMI/TPS 2023 conferenc

Infrared thermograms applied to near-field testing

Electromagnetic fields close to radiant structures can be measured quickly using an infrared camera. Examples of induced fields by wire antennas over a detection screen at distances shorter than one wavelength are presented. The measured thermograms agree with simulations that take into account heat propagation on the detection screen.Peer ReviewedPostprint (published version

Infrared thermograms applied to near-field testing

Electromagnetic fields close to radiant structures can be measured quickly using an infrared camera. Examples of induced fields by wire antennas over a detection screen at distances shorter than one wavelength are presented. The measured thermograms agree with simulations that take into account heat propagation on the detection screenPeer ReviewedPostprint (published version