AI 中文总结
研究针对新闻短视频音频的交叉验证方法，通过定义假设、计算对齐并进行似然比检验来实现，该方法计算快、更稳健且具可解释性，为音频篡改检测提供新视角与补充工具。
AI 中文摘要
本文探索了一种在特定背景下验证音频未被恶意篡改的方法，该背景为从新闻记录中截取的短视频。我们并非检测篡改痕迹，而是着重正向验证查询与新闻记录等可信源的一致性。我们提出一种针对短音频查询与参考记录进行交叉验证的方法，定义两个假设，计算每个假设下查询与参考的最可能对齐，然后对两个对齐进行似然比检验。实验表明该方法计算速度快，比使用MFCC特征和欧氏距离更稳健，且具有可解释性，为现有篡改检测方法提供了新视角和补充工具。
英文摘要
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.
CommentsPublished at ICASSP 2023
Journal refProc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1-5
DOI：10.1109/ICASSP49357.2023.10095059
