The Experts below are selected from a list of 9279 Experts worldwide ranked by ideXlab platform
Ioannis Pitas - One of the best experts on this subject based on the ideXlab platform.
-
robust image watermarking in the spatial domain
Signal Processing, 1998Co-Authors: Nikos Nikolaidis, Ioannis PitasAbstract:The rapid evolution of digital image manipulation and transmission techniques has created a pressing need for the protection of the intellectual property rights on images. A copyright protection method that is based on hiding an ‘invisible’ signal, known as digital watermark, in the image is presented in this paper. Watermark casting is performed in the spatial domain by slightly modifying the intensity of randomly selected image pixels. Watermark detection does not require the existence of the original image and is carried out by comparing the mean intensity value of the marked pixels against that of the pixels not marked. Statistical hypothesis testing is used for this purpose. Pixel modifications can be done in such a way that the watermark is resistant to JPEG compression and Lowpass Filtering. This is achieved by minimizing the energy content of the watermark signal at higher frequencies while taking into account properties of the human visual system. A variation that generates image dependent watermarks as well as a method to handle geometrical distortions are presented. An extension to color images is also pursued. Experiments on real images verify the effectiveness of the proposed techniques.
Michael Feldman - One of the best experts on this subject based on the ideXlab platform.
-
a signal decomposition or Lowpass Filtering with hilbert transform
Mechanical Systems and Signal Processing, 2011Co-Authors: Michael FeldmanAbstract:Abstract Recently, Chen and Wang discovered an explicit formula that makes use of the Hilbert transform for accurate decomposition of a lower harmonic from a signal composition. This letter presents another proof with a new interpretation for the formula using the Bedrosian identity for overlapping signals. This new and simpler proof is based only on the Hilbert transform and does not involve presentation of the Fourier transform. As a result the discovered formula is introduced as a Lowpass filter suitable for non-stationary signals.
Nikos Nikolaidis - One of the best experts on this subject based on the ideXlab platform.
-
robust image watermarking in the spatial domain
Signal Processing, 1998Co-Authors: Nikos Nikolaidis, Ioannis PitasAbstract:The rapid evolution of digital image manipulation and transmission techniques has created a pressing need for the protection of the intellectual property rights on images. A copyright protection method that is based on hiding an ‘invisible’ signal, known as digital watermark, in the image is presented in this paper. Watermark casting is performed in the spatial domain by slightly modifying the intensity of randomly selected image pixels. Watermark detection does not require the existence of the original image and is carried out by comparing the mean intensity value of the marked pixels against that of the pixels not marked. Statistical hypothesis testing is used for this purpose. Pixel modifications can be done in such a way that the watermark is resistant to JPEG compression and Lowpass Filtering. This is achieved by minimizing the energy content of the watermark signal at higher frequencies while taking into account properties of the human visual system. A variation that generates image dependent watermarks as well as a method to handle geometrical distortions are presented. An extension to color images is also pursued. Experiments on real images verify the effectiveness of the proposed techniques.
Emanuel Radoi - One of the best experts on this subject based on the ideXlab platform.
-
digital compensation of Lowpass filters imperfection in the modulated wideband converter compressed sensing scheme for radio frequency monitoring
Signal Processing, 2018Co-Authors: Lapluat Nguyen, Roland Gautier, Anthony Fiche, Gilles Burel, Emanuel RadoiAbstract:Abstract This paper focuses on non-ideal filters in a Modulated Wideband Converter (MWC) scheme. The MWC is a system that can sample a sparse wideband signal at sub-Nyquist rate. Generally, the output of the ideal MWC components will ensure a perfect reconstruction. In practice, the reconstruction should be based on the output of non-ideal components, especially filters. The impact of non-ideal filters will trigger to a bad reconstruction. In this paper, a detailed study on non-ideal Lowpass filters imperfection used in compressed sensing MWC scheme is synthesized. A digital post-treatment scheme with amplitude and phase compensation is proposed after real Lowpass Filtering step in order to have the filtered output as close as the ideal Lowpass filter output. At last, reconstruction spectra obtained from different simulated Lowpass filters are compared with different parameters of MWC.
Hwai-tsu Hu - One of the best experts on this subject based on the ideXlab platform.
-
Incorporating Spectral Shaping Filtering into DWT-Based Vector Modulation to Improve Blind Audio Watermarking
Wireless Personal Communications, 2017Co-Authors: Hwai-tsu HuAbstract:A spectral shaping technique emerging from autoregressive modeling is incorporated into vector modulation to achieve efficient blind audio watermarking. This technique allows the watermarking process to be performed in a broader frequency band with the embedding strength adapting to auditory masking thresholds. To ensure accurate watermark retrieval, we slacken the condition for binary embedding and develop an iterative algorithm to carry out energy-balanced vector modulation. As a result, the proposed scheme reaches a capacity as high as 818.26 bits per second but still possesses sufficient robustness and transparency. The effectiveness of the proposed scheme has been demonstrated using the perceptual evaluation of audio quality (PEAQ) and bit error rates of recovered watermarks. The PEAQ confirms that the watermarked audio signal is perceptually indistinguishable from the original one. Compared with other recently developed DWT-based methods with less payload capacities, the proposed scheme can achieve comparable, if not better, robustness for attacks such as resampling, requantization, amplitude scaling, noise corruption, Lowpass Filtering, DA/AD conversion, echo addition, jittering and MPEG-3 compression.
-
Supplementary Schemes to Enhance the Performance of DWT-RDM-Based Blind Audio Watermarking
Circuits Systems and Signal Processing, 2017Co-Authors: Hwai-tsu HuAbstract:This paper reports two schemes aimed at enhancing the performance of an algorithm based on rational dither modulation (RDM) for the blind watermarking of audio signals. The enhanced algorithm operates in a 4th-level approximation subband, which is obtained through discrete wavelet transform (DWT) decomposition. The first scheme involves the application of a two-tap FIR Lowpass filter to mask the noise induced by watermarking, while the second scheme is meant to compensate for distortion introduced by vector modulation. These two schemes make it possible for the DWT-RDM algorithm to formulate audio watermarking with a payload capacity of 689 bits per second. The embedding process is organized as parametric vector operations, which allow the implantation of binary bits into DWT coefficient vectors using two variables, respectively, corresponding to Lowpass Filtering and distortion compensation. Experiments aimed at evaluating the quality of the resulting audio files indicated an improvement in average ODG score from $$-0.33$$ - 0.33 to $$-0.26$$ - 0.26 . Furthermore, we observed a noticeable reduction in the bit error rates when conducting robustness tests against attacks, such as brumm addition, zero crossing, noise corruption, echo addition, MPEG-1 layer 3 compression, and time shifting.
