Multimedia forensics tasks based on cell-phone speech has always been a key research hotspot. However， the existing speech-based cell-phone identification tasks are all confined to the closed-set mode， which means that the training set and the test set share the same category set， which cannot guarantee the recognition accuracy for cell-phones of unknown categories， leading to the difficulty in applications of the existing methods to the unknown cell-phones. Therefore， an Open-set Source Cell-phone Identification method based on Feature interaction and representation enhancement （FireOSCI） was proposed. Firstly， a global information extraction block named GlobalBlock was designed on the basis of the multi-head attention block Fastformer for better capturing the global information from the whole speech sample and obtaining rich device feature information. Secondly， a local feature extraction block named LocalBlocks was presented on the basis of SE-Res2Block （Squeeze-Excitation Res2Block） to focus on enhancing cell-phone information related features and suppressing the features that are not related to the source cell-phone identification. Thirdly， an attention mechanism based feature fusion mechanism was designed to fuse global features with multi-layer local features deeply. Finally， a source cell?phone confirmation network was designed on the basis of attention pooling to improve the recognition accuracy in open-set mode. Comparison experimental results on cell-phone speech dataset with 13 different cell-phone brands and 86 different cell-phone models show that the proposed method can achieve identification of unknown categories of cell-phones， and provide a referable technical solution for the open-set recognition of speech-based source cell-phones.
