构建巴西政治音频伪造数据集，评估检测系统偏见与泛化能力
Cloned Voices, Real Consequences: Evaluating Bias in Political Deepfake Detection for Electoral Integrity in Brazil
- 基于巴西国会录音构建真实政治语音伪造数据集
- 现有检测模型在多样性语境下表现不稳，合成方式影响大于人口差异
- 为巴西选举安全提供关键评估基准，适合政策与技术研究者
生成式人工智能的发展使得选举期间伪造言论和放大政治虚假信息变得更容易。本文提出ParlaSpoof-BR，一个源自巴西众议院录音并扩展了多种文本转语音与语音转换模型合成语句的音频深度伪造数据集。利用该数据集，我们对主流音频深度伪造检测器进行基准测试，评估其在巴西葡萄牙语政治语料上的泛化能力，并探究预测中的潜在偏差。分析显示，当前系统在数据集所呈现的多样性中难以保持一致判断，方法学因素（如合成模型选择、篡改程度）的影响远超人口差异。ParlaSpoof-BR为在社会影响重大且研究不足的语境下研究音频伪造检测提供了领域特定基准，有助于提升巴西选举安全的检测系统鲁棒性。
原文摘要 · Abstract (English)
Recent advances in generative artificial intelligence have made it easier to fabricate statements and amplify political disinformation during elections. We introduce ParlaSpoof-BR, an audio deepfake dataset derived from recordings of the Brazilian Chamber of Deputies and expanded with synthetic utterances from diverse text-to-speech and voice conversion models. Using ParlaSpoof-BR, we benchmark state-of-the-art audio deepfake detectors, examine their ability to generalize to Brazilian Portuguese political speech, and investigate potential biases in their predictions. Our analysis reveals that current systems struggle to provide consistent decisions across the diversity represented in the dataset, with methodological factors (synthesis model choice, manipulation extent) dominating over demographic disparities. ParlaSpoof-BR provides a domain-specific benchmark for studying audio deepfake detection in a socially consequential and underrepresented setting, supporting the development of more robust detection systems for electoral integrity in Brazil.
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