Abstract
Copy-move forgery is one of the common means of audio splicing. With the advantages of convenience and covertness, copy-move forgery is widely used in our daily life, which is difficult to be found out. This paper proposes a multi-feature decision method to detect copy-move forgery. We use C4.5 decision tree to merge together the detection result of four features which are gammatone feature, Mel-frequency cepstral coefficients (MFCCs) feature, pitch feature and discrete fourier transform (DFT) coefficients. To enhance the detection effect, we use Pearson correlation coefficients (PCCs) and average difference (AD) to evaluate the detection result of these four features. Compared with other single-feature decision methods, the proposed scheme has better detection result of copy-move forgery. The experiment shows that the proposed scheme is effective and robust with the data of 1000 normal audio and 1000 copy-move audio.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
