AUDIO COPY-MOVE FORGERY DETECTION WITH MACHINE LEARNING METHODS

Abstract

Converting original sounds into fake sounds using various methods and using these sounds for fraud or misinformation purposes poses serious risks and threats. In this study, a classification system using machine learning methods is created and performance analysis is performed in order to detect sounds created with copy-move forgery, which is one of the types of sound forgery. Sound files are treated as raw data. Then, Mel-spectrograms are obtained to visually represent the spectral features of the sound over time. Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN) and XGBoost algorithms are used in the classification phase. As a result of the performance analysis of the created models, the highest success is achieved with the XGBoost algorithm. The performance of the XGBoost algorithm is further improved by performing hyperparameter optimization with the Random Search method. The results of the models are analyzed using various metrics. According to the study results, it is seen that it gives competitive results with the XGBoost algorithm.

Keywords

• Audio
• Copy-Move Forgery
• Machine Learning
• XGBoost

References

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