Fig 6 - uploaded by Talbi Mourad
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First example of image watermarking: (a) Cover image: Baby, (b) Watermarked image of Baby (c) Watermark logo: speech image, (d) Extracted Watermark: Extracted Speech Image.
Source publication
Background
In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image.
Methods
It also uses signature in the embedding and extra...
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Citations
... This algorithm has better characteristic information retention of the ECG signal and owns a higher SNR and achieves a better denoising effect [9]. This paper presents a new ECG denoising technique based on Lifting Wavelet Transform ( LWT ) [7,25,26] and Total Variation Minimization ( TVM ) [4]. The remaining paper organization is in the following manner: Section 2 elucidates LWT . ...
... As previously discussed, the paper proposes a new ECG denoising technique based on LWT [7,25,26] and TVM [4]. The first step of this technique consists in applying the LWT to the noisy ECG Signal (where 2 is the decomposition level) for obtaining three wavelet sub-bands, cD 1 , cD 2 and cA 2 . ...
In this paper, we propose a new technique of Electrocardiogram (ECG) denoising based on Lifting Wavelet Transform (LWT) and Total Variation Minimization (TVM). The first step of this technique consists in applying the LWT to the noisy ECG signal for obtaining three wavelet sub-bands, cD1, cD2 and cA2. The coefficients cD1 and cD2 are details coefficients, denoised by soft thresholding for obtaining two denoised coefficients, cDd1 and cDd2. The coefficient cA2 is an approximation coefficient, denoised by TVM based denoising method for obtaining a denoised coefficient cAd2. The last step of this technique consists in applying the inverse of LWT to cDd1, cDd2 and cAd2 for obtaining the denoised ECG signal. The proposed technique evaluation is by comparing it with three other denoising approaches discussed in the extant in literature. The TVM-based approach is the first, the second is 1-D double-density complex Discrete Wavelet Transform denoising method, and the third is the ECG denoising technique based on Non-local Means. These four mentioned techniques are applied to various ECG signals taken from MIT-BIH database. Those signals are degraded by an additive White Gaussian Noise at different values of Signal to Noise Ratio (SNRidB). The results obtained from the calculation of SNR and the Mean Square Error, infer that the proposed technique outperforms the other denoising approaches selected for the comparative evaluation.
... In [6], a steganographic method based on the singular value decomposition (SVD) for speech signals is presented. Firstly, the speech signal is transformed into a gray-scale image, then, the singular value decomposition (SVD) is applied to the speech gray-scale image; also, the SVD is applied to the HH1 subband of the first level of the lifting wavelet transform (LWT) of the color carrier image. ...
... Talbi [7] presents a steganographic method based on [6], which additionally uses an encryption technique to increase the security of the hidden speech signal. Experimental results obtained in [6,7] have demonstrated low embedding capacity, and regular performance in the quality of the stegoimage (PSNR ≈ 40 dB) and the quality of the extracted speech signal (SNR of 25 dB) for maximum 8-second recording speech signals using 512×512 carrier color images. ...
... Talbi [7] presents a steganographic method based on [6], which additionally uses an encryption technique to increase the security of the hidden speech signal. Experimental results obtained in [6,7] have demonstrated low embedding capacity, and regular performance in the quality of the stegoimage (PSNR ≈ 40 dB) and the quality of the extracted speech signal (SNR of 25 dB) for maximum 8-second recording speech signals using 512×512 carrier color images. ...
In recent years, the evolution and expansion of the multimedia world has allowed digital images to be widely used as a carrier medium for facilitating the sending and receiving of confidential digital content such as text, audio and images. However, there are few steganographic schemes that hide digital speech signals into digital images; also, the state-of-the-art methods have some disadvantages such as low embedding capacity, low quality in the modified image, and/or low audible quality in the recovered speech signal. In this paper, a new steganographic scheme that hides a digital speech signal into a color image is presented, where the embedding process is performed in the HL and LH channels of the first decomposition level of the discrete wavelet transform (DWT) over the YCbCr color space; additionally, a chaotic map method is implemented to encrypt the speech signal and to spread it throughout the whole image. Experimental results have demonstrated that the proposed scheme results in better performance against other state-of-the-art methods, due to their ability to embed and recover speech signals with duration up to 16.384 seconds using color images of 512×512 pixels, obtaining 32 dB and 0.92 in PSNR and SSIM, respectively for the stego-image, and 41 dB in SNR for the recovered speech signal.
... The final estimate is obtained by averaging those estimates [1]. In this paper we propose a new denoising technique based on Lifting Wavelet Transform ( ) [20,21,22] and [23]. The rest of this paper is organized as follow: in section 2, we will deal with the Lifting Wavelet Transform ( ). ...
... Denoising Technique As previously mentioned, in this paper we propose a new technique of Denoising based on Lifting Wavelet Transform ( ) [20,21,22] and Total Variation Minimization ( ) [23]. This technique consist at first step in applying the to the noisy Signal (where 2 is the decomposition level) in order to obtain three wavelet sub-bands, , and . ...
Buckground:
The signal of Electrocardiogram (•) is one of the most popular diagnostic means providing an electrical picture of the heart and also information about different pathological conditions. Due to the path deformities and external electrical disturbances, the signal of becomes noisy. Hence, in literature, many ECG denoising algorithms have been proposed and among them we can mention the techniques based on wavelet coefficient shrinkage. The purpose of this paper is to denoise ECG signals applying a new ECG Denoising technique and proving its performance compared to some denoising approaches existing in literature. This new proposed technique of B Denoising consists at the first step in applying the Lifting Wavelet Transform (u) to the noisy c Signal (where 2 is the decomposition level) in order to obtain three noisy wavelet sub-bands, k, g and r. The two coefficients, o, u are details ones and they are denoised by soft or hard thresholding in order to obtain denoised coefficients, n1 and d2. The coefficient : is an approximation one and is denoised by Total Variation Minimization in order to obtain a denoised one, T2. Finally, the inverse of his applied to e, and s in order to obtain the denoised i signal. The evaluation of this proposed technique is performed by comparing it to three other denoising approaches existing in literature. The first one of these approaches is based on g, the second one is n double-density complex a denoising method and the third one is based on non local means.
Results:
All These techniques are applied on a number of lsignals taken from database and corrupted by an additive White Gaussian noise at different values of Signal to Noise Ratio (o). The obtained results from the computation of the f and the Mean Square Error ( ), show that the proposed technique outperforms the other three mentioned techniques.
Conclusion:
In this paper, the proposed ECG denoising technique based on E and l, outperforms the other previously mentioned denoising approaches and this based on the computation of the SNR and MSE.
... It consists at first step in encrypting the watermark before inserting the obtained encrypted watermark into the host image. Encryption is performed via Arnold Cat Map [2] and the embedding is performed via a secure watermarking scheme based on LWT À SVD [5]. In the rest of this paper we will detail the LWT À SVD based image watermarking approach proposed in [5]. ...
... Encryption is performed via Arnold Cat Map [2] and the embedding is performed via a secure watermarking scheme based on LWT À SVD [5]. In the rest of this paper we will detail the LWT À SVD based image watermarking approach proposed in [5]. After that, we will detail our proposed encryption-watermarking scheme for embedding speech signals into digital images. ...
... In our previous research works, we have proposed some techniques of speech signal embedding into digital image [5,6]. Among them, we will detail the technique based on LWT À SVD [5]. ...
In the present article, is proposed a novel encryption-watermarking approach. It is applied for embedding speech signals into digital images. It consists at first step in encrypting the watermark. After that, the obtained encrypted watermark is embedded into the host image. This watermark encryption is performed employing Arnold cat map and the embedding is performed employing a secure image watermarking technique based on \( {\text{LWT}} - {\text{SVD}} \). We evaluate the performance of this proposed technique by comparing it to an existing encryption watermarking approach called \( {\text{DSAWM}} \). The latter is also a secure watermarking scheme based on \( {\text{DWT}} - {\text{SVD}} \). It also applies Arnold Cat Map for encrypting the watermark at first step and then inserting the obtained encrypted watermark into the cover image. Evaluation and comparison of the two techniques are performed by computing the \( SegSNR, PESQ, PSNR, RMSE \), \( {\text{pcc}} \), \( MAE \) and \( SSIM \). The obtained results from this evaluation show the performance of this proposed encryption-watermarking approach. In fact, the PSNR, \( {\text{RMSE}} \), \( {\text{pcc}} \), \( {\text{MAE}} \) and \( {\text{SSIM}} \) values show a good perceptual quality of the watermarked images. Moreover, the \( {\text{SegSNR}} \) and \( {\text{PESQ}} \) values show a very good perceptual quality of the speech signals reconstructed after watermark extraction and decryption. We also tested the robustness of the proposed technique by applying three sorts of attacks on the watermarked images. Those attacks are Median, \( {\text{JPEG}} \) Compression and additive White Noise attacks. The results obtained from calculation of the \( {\text{SNR}} \), \( {\text{PESQ}}, {\text{PSNR}} \) and \( {\text{SSIM}} \), show the robustness of this proposed approach against those attacks.
In this chapter, we will detail our approach of ElectrocardiogramElectrocardiogram (ECG)(ECG) denoisingDenoising based on Lifting WaveletWaveletsTransformLifting Wavelet Transform (LWT) and Total Variation MinimizationTotal variation minimization (TVM). This approach was proposed in the literature and its first step consists in applying twice the LWT to the noisy ECG signal in order to obtain two details coefficients, cD1 (at first level) and cD2 and one approximation coefficient cA2 (at second level). The two coefficients cD1 and cD2 are denoised by soft thresholding in order to have two denoised coefcients cDd1 and cDd2. The coefficient cA2 is denoised by TVM-based denoising technique in order to have a denoised coefficient cAd2. The last step of this proposed approach consists in applying twice the inverse of LWT (LWT−1) to cDd1 and cDd2 and cAd2 in order to obtain the denoised ECGElectrocardiogram (ECG) signal. The proposed approach evaluation is performed by comparing it to three other denoising techniques introduced in the literature. The TVMTotal variation minimization-based approach is the first one, the second one is the 1D double-density complex Discrete Wavelet TransformDiscrete Wavelet TransformdenoisingDenoising method, and the third one is the ECG denoising technique based on Non-local Means. These four techniques mentioned above are applied to various ECGElectrocardiogram (ECG) signals belonging to MIT-BIH database. Those signals are corrupted by an Additive White Gaussian Noise at different values of Signal to Noise Ratio before denoising, SNRidB. The results obtained from the calculation of SNR and the Mean Square Error show that the proposed technique outperforms the other denoising approaches used for this comparative evaluation.
