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Partially Edited Audio: Generation, Defense, and Open Problems

Year: 2026
Type: preprint
Abstract: Recent advances in generative audio models facilitate increasingly flexible and selective editing of linguistic content and paralinguistic, extralinguistic, and other attributes. This paradigm, named partially edited audio, differs from full audio generation as only portions of the real recording are edited. This technique can serve both beneficial and harmful applications. We therefore present a concise survey from both generation and defense perspectives, focusing on audio, as a contribution to the ICASSP 2027 special session entitled "Partially Edited Media: Perspectives from Synthesis and Defense." We first define partially edited audio and argue that what counts as ``fake'' or harmful is an application-dependent judgment, not a fixed property of the edit. We then survey audio editing by editing target, approaches, and evaluation, and then defense methods by detection, localization, diarization, and interpretability, summarizing representative benchmarks and toolkits. Finally, we discuss future directions and open problems.
DOI: 10.48550/arxiv.2610.37640
Authors: Zhang, Lin, Zhang, You, Evans, Nicholas, Wang, Xin, Harwath, David, Dhall, Abhinav, Shi, Bowen, Sun, Chengzhe, Lyu, Siwei, Khudanpur, Sanjeev
Institutions: Télécom Paris, National Institute of Informatics, The University of Texas at Austin, Monash University, University at Buffalo, State University of New York, Johns Hopkins University
Language: English
Open Access status: green
OpenAlex ID: W7224078660
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