Abstract: Provided is a method and apparatus for support recovery of jointly sparse signals from a plurality of snapshots, thereby enhancing a capability for reconstructing a support in a variety of circumstances, by providing enhanced robustness against noise and perturbation, and/or enhanced computational efficiency. The method may include partial support recovery using a compressed sensing-multiple measurement vector (CS-MMV) scheme; and a complementary support recovery and sparsity level estimation. The complementary support recovery may use subspace information extracted from the plurality of snapshots and partial support information. The total number of elements in the partial support and in the complementary support may be equal to the sparsity level.

Abstract: Disclosed a method of designing a watermark having the power spectral density optimized so that the detection performance can be improved by employing the whitening filtered detection after the Wiener attack. The power spectral density of the watermark is designed using an optimization method that can improve the entire detection performance by reflecting the gain of the whitening filter after the Wiener attack. A higher detection gain is obtained using the whitening filter after the Wiener attack, and the expected value of the difference between test statistics of the two hypotheses that the watermark exists and the watermark does not exist, respectively, is maximized to optimize the detection performance. Regarding the expected value of the difference between the test statistics as an objective function, the power spectral density of the watermark, which corresponds to a maximum differentiated value of the power spectral density of the watermark using the Lagrange multiplier method, is obtained.

Abstract: An apparatus and method for digital watermarking using nonlinear quantization are provided. The apparatus includes: an input signal processing unit which receives an original signal into which a watermark is to be embedded, performs discrete Fourier transform (DFT) of the signal, and outputs the result in predetermined number of subband units; a psychoacoustic model unit which receives the DFT coefficients and calculates a signal to mask ratio (SMR); a watermark embedder which embeds the watermark through nonlinear quantization of the DFT coefficients, which correspond to the predetermined middle frequency band, using the quantizer step size determined by the SMR; and a synthesizing unit which combines each subband except the middle frequency band and the output signal of the quantization unit, performs inverse DFT, and outputs the result. The watermarking method based on nonlinear quantization is robust against both amplitude modification and lossy compression.

Abstract: Disclosed is an amplitude-scaling resilient audio watermarking apparatus and method. An encoding apparatus includes: a polyphase filterbank for dividing an audio signal into a plurality of subbands; a psychoacoustic module for applying a psychoacoustic model to the audio signal to provide a signal-to-mask ratio; a watermark encoder for evaluating an encoding parameter from the subbands according to the signal-to-mask ratio and embedding the encoding parameter and a watermark into subbands corresponding to the middle frequency; and a synthesis filterbank for synthesizing the subband signals to output a watermarked audio signal.

Abstract: Disclosed a method of designing a watermark having the power spectral density optimized so that the detection performance can be improved by employing the whitening filtered detection after the Wiener attack. The power spectral density of the watermark is designed using an optimization method that can improve the entire detection performance by reflecting the gain of the whitening filter after the Wiener attack. A higher detection gain is obtained using the whitening filter after the Wiener attack, and the expected value of the difference between test statistics of the two hypotheses that the watermark exists and the watermark does not exist, respectively, is maximized to optimize the detection performance. Regarding the expected value of the difference between the test statistics as an objective function, the power spectral density of the watermark, which corresponds to a maximum differentiated value of the power spectral density of the watermark using the Lagrange multiplier method, is obtained.