An AAC steganography scheme for adaptive embedding with distortion minimization model

Nowadays, most prevailing approaches to advanced audio coding (AAC) steganography are content non-adaptive which have low embedding capacity and poor security. In this paper, we construct a new distortion by integrating the uniform embedding distortion with the masking threshold, and propose an adaptive steganographic scheme for AAC audio by modifying quantized modified discrete cosine transform (QMDCT) coefficients. To hold the statistical distribution of QMDCT coefficients we introduce uniform embedding idea. Then the psychoacoustic model is used to avoid the declining of hearing quality. Finally, we minimize the overall embedding distortion by utilizing syndrome-trellis codes (STCs) technique and the defined distortion function. Comprehensive experimental results show that the proposed scheme achieves better performance than related works. The detection error of 128-kbps-AAC dataset is higher than 24.78% when the embedding payload reaches 3.70 kbps, which is significantly lower than the state-of-the-art AAC steganographic methods.