An SVD-based adaptive robust speech steganography using MDCT coefficient

Speech is one of the essential ways of communication. The study of speech steganography provides great value in information security. To improve imperceptibility and robustness of speech steganography, the characteristics of speech signals should be fully taken into account. In this paper, a robust speech steganographic scheme based on Singular Value Decomposition (SVD) and Modified Discrete Cosine Transform (MDCT) is proposed. Firstly, Voice Activity Detector (VAD) is used to detect voiced frames from speech signals, along with MDCT with Kaiser Bessel Derived (KBD) window being performed on each frame. Then the MDCT coefficients are selected from a certain frequency range and divided into a pair of segments. The two largest singular values of the paired segments are modified respectively according to their value difference to embed secret message. The thresholds are adaptively adjusted according to the largest singular values. Extensive experiments are carried out to compare the proposed method with three other methods from imperceptibility, robustness, capacity, and security. The experimental results show that under the simulation parameters β = 320, Nk = 58, fl = 100 Hz, fh = 3 kHz, and α = 0.61, the proposed method has striking advantages to resist common robust attacks and the state-of-the-art steganalysis attacks while maintaining good imperceptibility.