Advanced audio coding steganography algorithm with distortion minimization model based on audio beat. (March 2023) - Zhang, Xue; Li, Chen; Tian, Lihua
Abstract: Currently, most advanced audio coding (AAC) steganography methods are content-non-adaptive without considering the characteristics of audio, and there are several limitations in imperceptibility and steganalysis. In this paper, we use an audio feature beat as the anchor point to identify the cover elements, group the quantized modified discrete cosine transform (QMDCT) coefficients in the small value area, and finally use the syndrome-trellis codes (STCs) framework for content-adaptive embedding to obtain the minimum distortion. In the STCs framework, we comprehensively consider auditory and data distortions. Experimental results demonstrate that the proposed steganography algorithm has a 10% improvement over the compared algorithms in terms of imperceptibility and steganalysis, and it can accurately extract secret information in face of frame loss and misalignment. Highlights: A neural network is adopted to track the beats. New distortion function of STCs framework is proposed. Quantized modified discrete cosine transform coefficients in the small area are designed to be cover elements by beats. Content-adaptive embedding algorithm is proposed.