The proliferation of DeepFake has engendered widespread societal concerns, positioning its detection as a pressing imperative. Although existing studies have utilized single-subject EEG to distinguish between real and AI-generated content (AIGC), there is still a lack of research exploring dual-brain EEG and multimodal experimental paradigms. This study introduced a novel experimental paradigm, employing EEG hyperscanning to construct a dyadic EEG dataset for AIGC detection. This study employed inter-subject correlation (ISC) analysis to investigate the differences of interpersonal neural synchronization (INS). Additionally, this study proposed a novel neural network model named Squeeze-and-Excitation Depthwise Separable Convolution (SEDSC) for predicting the authenticity of real vs. AIGC. ISC analysis revealed apparent differences in INS under different modalities, valences, and animacy. Specifically, across the four frequency bands, both text and audio modalities elicited higher inter-brain synchronization under real materials than under AIGC materials. SEDSC utilized the phase locking value to assess inter-brain functional connectivity and weighted the inputs from four frequency bands before feeding them into the network for classification. This approach achieved a classification accuracy of 92.42% in distinguishing real from fake content. This study designed a new experimental paradigm and constructed a dataset, confirming that there were evident differences in INS during tasks involving real and AIGC materials. Furthermore, SEDSC successfully predicted the authenticity of the content.