Partially edited audio: Generation, defense, and open problems

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 8Johns Hopkins University production editing of podcasts. Meanwhile, there are also potentially harmful or nefarious applications when misused, such as spreading misinformation, the manipulation of elections, fraud, extortion, reputational damage, or breaching security systems. From the defense perspective, such manipulations are less perceptually salient, more difficult to detect, and potentially more harmful in security-sensitive scenarios. For example, partial editing may be used to create deepfakes [7, 8], thereby resulting in more severe and potentially irreversible consequences. “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.