S-Faster R-CNN: Intra-Spectral Similarity Learning for Audio Copy-Move Forgery Localization in IoT Security

In the Internet of Audio Things, communication security of the audio control terminal is vulnerable to copy-move threats, and detecting and locating audio copy-move forgery remains challenging nowadays. The forgery detection method based on deep learning achieves higher detection accuracy but fails to localize forged regions. To address this issue, this article proposes an S-Faster R-CNN model for audio copy-move forgery detection and localization. We integrate a novel Similarity Computation Module (SCM) into the Faster R-CNN framework, forming the S-Faster R-CNN model. Obtaining the integration of the SCM, which allows the S-Faster R-CNN to precisely localize forgery regions within the spectrogram. Finally, the image coordinate transformation algorithm is used to map these forged regions to the corresponding locations of the original audio waveform, thus completing the audio copy-move forgery detection and localization. Evaluated on three datasets, our method achieves an average recall of 90%, an average precision of 84%, and an average F1-score of 87%, respectively. Experimental results indicate that the S-Faster R-CNN outperforms state-of-the-art methods in both forgery detection accuracy and especially in localization. Moreover, the proposed method shows good robustness under multiple post-processing.