A study on audio synchronous steganography detection and distributed guide inference model based on sliding spectral features and intelligent inference drive

Abstract: With the rise of short video platforms in global communication, embedding steganographic data in audio synchronization streams has emerged as a new covert communication method. To address the limitations of traditional techniques in detecting synchronized steganography, this paper proposes a detection and distributed guidance reconstruction model based on short video "Yupan" samples released by China's South Sea Fleet on TikTok. The method integrates sliding spectrum feature extraction and intelligent inference mechanisms. A 25 ms sliding window with short-time Fourier transform (STFT) is used to extract the main frequency trajectory and construct the synchronization frame detection model (M1), identifying a frame flag "FFFFFFFFFFFFFFFFFF80". The subsequent 32-byte payload is decoded by a structured model (M2) to infer distributed guidance commands. Analysis reveals a low-entropy, repetitive byte sequence in the 36 to 45 second audio segment with highly concentrated spectral energy, confirming the presence of synchronization frames. Although plaintext semantics are not restored, the consistency in command field layout suggests features of military communication protocols. The multi-segment splicing model further shows cross-video embedding and centralized decoding capabilities. The proposed framework validates the effectiveness of sliding spectral features for synchronized steganography detection and builds an extensible inference model for covert communication analysis and tactical guidance simulation on open platforms.