Audio Tampering Detection

Audio tampering detection aims to identify and locate manipulations in digital audio recordings, a crucial task given the increasing ease of audio forgery. Current research focuses on developing robust algorithms, often employing deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as BiLSTMs), to extract both shallow and deep features from audio signals, including those based on electrical network frequency (ENF) analysis. These methods are designed to improve detection accuracy and localization precision, even in the presence of noise or sophisticated post-processing techniques. The advancements in this field have significant implications for forensic science, digital media authentication, and ensuring the integrity of audio evidence.