In the current paper we present a framework for providing supervisory tools for multimedia Content Authentication As A Service (CAAAS). A double compression method for discontinuity detection in audio signals is implemented and integrated in the provided web service. The user can upload audio/video content or provide links and thereafter, a feature vector is extracted from the audio modality of the selected content for the investigation of discontinuities of the signal via the proposed algorithms. Several visualizations are returned to the user, indicating possible points of forgery in the audio/visual file. Moreover, an audio tampering detection methodology by unsupervised clustering of short-window non-vocal segments, in order to identify differentiations of the acoustic environment of speech signals is presented and evaluated.
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