Blind Detection of Copy-Move Forgery in Digital Audio Forensics

Although copy-move forgery is one of the most common fabrication techniques, blind detection of such tampering in digital audio is mostly unexplored. Unlike active techniques, blind forgery detection is challenging, because it does not embed a watermark or signature in an audio that is unknown in most of the real-life scenarios. Therefore, forgery localization becomes more challenging, especially when using blind methods. In this paper, we propose a novel method for blind detection and localization of copy-move forgery. One of the most crucial steps in the proposed method is a voice activity detection (VAD) module for investigating audio recordings to detect and localize the forgery. The VAD module is equally vital for the development of the copy-move forgery database, wherein audio samples are generated by using the recordings of various types of microphones. We employ a chaotic theory to copy and move the text in generated forged recordings to ensure forgery localization at any place in a recording. The VAD module is responsible for the extraction of words in a forged audio, and these words are analyzed by applying a 1-D local binary pattern operator. This operator provides the patterns of extracted words in the form of histograms. The forged parts (copy and move text) have similar histograms. An accuracy of 96.59% is achieved, and the proposed method is deemed robust against noise.