Methods of covert communication of speech signals based on a bio-inspired principle

This work presents two speech hiding methods based on a bio-inspired concept known as the ability of adaptation of speech signals. A cryptographic model uses the adaptation to transform a secret message to a non-sensitive target speech signal, and then, the scrambled speech signal is an intelligible signal. The residual intelligibility is extremely low and it is appropriate to transmit secure speech signals. On the other hand, in a steganographic model, the adapted speech signal is hidden into a host signal by using indirect substitution or direct substitution. In the first case, the scheme is known as Efficient Wavelet Masking (EWM), and in the second case, it is known as improved-EWM (iEWM). While EWM demonstrated to be highly statistical transparent, the second one, iEWM, demonstrated to be highly robust against signal manipulations. Finally, with the purpose to transmit secure speech signals in real-time operation, a hardware-based scheme is proposedEsta tesis presenta dos métodos de comunicación encubierta de señales de voz utilizando un concepto bio-inspirado, conocido como la “habilidad de adaptación de señales de voz”. El modelo de criptografía utiliza la adaptación para transformar un mensaje secreto a una señal de voz no confidencial, obteniendo una señal de voz encriptada legible. Este método es apropiado para transmitir señales de voz seguras porque en la señal encriptada no quedan rastros del mensaje secreto original. En el caso de esteganografía, la señal de voz adaptada se oculta en una señal de voz huésped, utilizando sustitución directa o indirecta. En el primer caso el esquema se denomina EWM y en el segundo caso iEWM. EWM demostró ser altamente transparente, mientras que iEWM demostró ser altamente robusto contra manipulaciones de señal. Finalmente, con el propósito de transmitir señales de voz seguras en tiempo real, se propone un esquema para dispositivos hardware

Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding

This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the biorthogonal base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.Peer Reviewe