标题： 并非所有深度伪造都相同：对音频伪造进行优先处理以实现稳健的深度伪造歌手识别 显示英文标题

标题： Not All Deepfakes Are Created Equal: Triaging Audio Forgeries for Robust Deepfake Singer Identification

摘要： 高度逼真的歌唱语音深度伪造的泛滥给保护艺术家形象和内容真实性带来了重大挑战。 在语音深度伪造中自动识别歌手是一种有前景的途径，使艺术家和权利持有人能够对抗未经授权使用其声音，但仍然是一个开放的研究问题。 基于最有害的深度伪造是最高质量的这一前提，我们引入了一个两阶段的流程来识别歌手的语音相似性。 它首先使用一个判别器模型来过滤掉那些无法准确再现语音相似性的低质量伪造品。 随后的模型仅在真实录音上进行训练，用于识别剩余高质量深度伪造和真实音频中的歌手。 实验表明，该系统在真实和合成内容上始终优于现有的基线方法。 显示英文摘要

摘要： The proliferation of highly realistic singing voice deepfakes presents a significant challenge to protecting artist likeness and content authenticity. Automatic singer identification in vocal deepfakes is a promising avenue for artists and rights holders to defend against unauthorized use of their voice, but remains an open research problem. Based on the premise that the most harmful deepfakes are those of the highest quality, we introduce a two-stage pipeline to identify a singer's vocal likeness. It first employs a discriminator model to filter out low-quality forgeries that fail to accurately reproduce vocal likeness. A subsequent model, trained exclusively on authentic recordings, identifies the singer in the remaining high-quality deepfakes and authentic audio. Experiments show that this system consistently outperforms existing baselines on both authentic and synthetic content.