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2024 47th International Conference on Telecommunications and Signal Processing (TSP)

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ArCapsNet for Audio Splicing Forgery Detection

Complex forgeries such as deepfakes, which are very popular today, as well as very simple but effective manipulation techniques, the production of which does not require the use of deep networks such as GAN, are still practiced. Audio splicing forgery, which combines multiple speech segments from different recordings of a person to alter the content of a speech recording, is one of these manipulation techniques and presents a great challenge to audio forgery. In this paper, a novel audio splicing detection method based on ArCapsNet architecture is proposed. The proposed method consists of two stages. In the first stage, the audio file given as input is converted into a cochleagram image. In the second stage, features are extracted from the cochleagram images with EfficientNet and the ArCapsNet is trained with these features. As a result of the training, the audio files given as a test are labelled as forged/original. The proposed method is tested on the database created by us. The results obtained are quite high on the database.

Beste Ustubioglu
Karadeniz Technical University

Samet Dinçer
Karadeniz Technical University

Arda Ustubioglu
Trabzon University

Guzin Ulutas
Karadeniz Technical University


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