AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

• Autor(a) principal: Sampaio, José Fabrizio Pereira
• Data de Publicação: 2019
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Revista Brasileira de Ciências Policiais (Online)
• Texto Completo: https://periodicos.pf.gov.br/index.php/RBCP/article/view/534
• topic: Perícias; Multimídia Forense; Autenticidade de Áudio.

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks.