Audio Cross Verification Using Dual Alignment Likelihood Ratio Test
Audio Cross Verification Using Dual Alignment Likelihood Ratio Test
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DOI：
10.1109/icassp49357.2023.10095059
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Heidi Lei;Arm Wonghirundacha;Irmak Bukey;T. Tsai
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文献类型：
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作者：
Heidi Lei;Arm Wonghirundacha;Irmak Bukey;T. Tsai
本文探讨了一种验证音频在特定环境下没有被恶意篡改的方法：从新闻录音中截取的短病毒视频。我们没有尝试检测篡改工件（内部不一致），而是专注于针对可信源（如新闻记录）积极验证查询（外部一致性）。我们提出了一种方法来交叉验证一个短音频查询从它被采取的参考录音。我们的方法是定义两个假设（未篡改vs篡改），计算每个假设的查询和引用之间最可能的对齐，然后对这两个对齐执行似然比检验。我们表明，该方法计算速度快，比使用欧几里得距离的MFCC特征更具鲁棒性，并且具有可解释性的关键优点。我们的交叉验证方法为现有的篡改检测方法提供了另一种视角和补充工具。
This paper explores a way to verify that audio has not been maliciously tampered in a specific context: short viral videos taken from news recordings. Rather than trying to detect artifacts of tampering (internal inconsistency), we focus on positively verifying a query against a trusted source such as a news recording (external consistency). We propose a method for cross verifying a short audio query against a reference recording from which it was taken. Our approach is to define two hypotheses (non-tampered vs tampered), calculate the most likely alignment between query and reference for each hypothesis, and then perform a likelihood ratio test on the two alignments. We show that this method is fast to compute, much more robust than using MFCC features with Euclidean distance, and has the key benefit of explainability. Our cross verification approach provides an alternative perspective and complementary tool to existing tampering detection methods.