Distingusic: Distinguishing Synthesized Music from Human

In this paper we focus on a problem that is increasingly plaguing the music industry; to a large extent due to the proliferation of generative AI models that enable the generation of new realistic and indistinguishable content for diverse modalities: text, image, audio, video. We address this problem from the perspective of audio watermarking; to our best knowledge, this is the first-known watermarking based approach to solve the problem of distinguishing realistic songs synthesized from generative AI models from real songs sung by humans. In more detail, our approach specifically utilizes the SHA-256 hash function, Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for robust audio watermarking of synthesized songs. Before embedding, the audio is subjected to an attack phase to pinpoint less vulnerable regions for QR watermark placement. During the embedding process, the audio chunks first undergo a 1-level Discrete Wavelet Transform (DWT), and then the resulting approximate coefficients go through Singular Value Decompo-sition (SVD). Additionally, the watermarked array is subjected to SHA-256 hashing for collision-resistant conciseness, which is subsequently embedded into the singular values of the audio. Experimental findings demonstrate the superiority of our method over existing audio watermarking approaches under various signal attack scenarios.