Rajesh Mehta

Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

This paper presents an imperceptible, robust, secure and efficient image watermarking scheme in lifting wavelet domain using combination of genetic algorithm (GA) and Lagrangian support vector regression (LSVR). First, four subbands low–low (LL), low–high (LH), high–low (HL) and high–high (HH) are obtained by decomposing the host image from spatial domain to frequency domain using one level lifting wavelet transform. Second, the approximate image (LL subband) is divided into non overlapping blocks and the selected blocks based on the fuzzy entropy are used to embed the binary watermark. Third, based on the correlation property of each transformed selected block, significant lifting wavelet coefficient act as target to LSVR and its neighboring coefficients (called feature vector) are set as input to LSVR to find optimal regression function. This optimal regression function is used to embed and extract the scrambled watermark. In the proposed scheme, GA is used to solve the problem of optimal watermark embedding strength, based on the noise sensitivity of each selected block, in order to increase the imperceptibility of the watermark. Due to the good learning capability and high generalization property of LSVR against noisy datasets, high degree of robustness is achieved and is well suited for copyright protection applications. Experimental results on standard and real world images show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against geometric and non geometric attacks like JPEG compression, median filtering, average filtering, addition of noise, sharpening, scaling, cropping and rotation compared with the state-of-art techniques.

A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform

In this paper, an efficient image watermarking scheme based on Lagrangian support vector regression (LSVR) and lifting wavelet transform (LWT) is proposed to balance the trade-off between imperceptibility and robustness. LWT is faster and efficient implementation of traditional wavelet transform and LSVR has faster learning speed and high generalization capability compared to classical support vector regression. Combination of LWT and LSVR based image watermarking not only show imperceptibility and robustness but also reduces the time complexity. First the image is decomposed into four subbands using one level LWT and low frequency subband (approximate image) is partitioned into non overlapping blocks. Selected blocks based on correlation of wavelet coefficients are used to embed the binary watermark logo. For each selected block, significant wavelet coefficient is used as target to LSVR and its neighboring coefficients (feature vector) act as input to LSVR. The security of the watermark is achieved by applying Arnold transformation to original watermark. The scrambled watermark bit is embedded by comparing the output (predicted value) of LSVR of each selected block and the actual target value. The good learning capability and high generalization property of LSVR against noisy datasets provides the successful extraction of watermark against several image processing attacks: median filtering, average filtering, addition of Gaussian noise, salt and pepper noise, contrast enhancement, blurring, scaling, cropping and rotation. Experimental results show that the proposed scheme gives significant improvement in imperceptibility and robustness compared to the state-of-art techniques.

A hybrid semi-blind gray scale image watermarking algorithm based on DWT-SVD using human visual system model

To achieve good imperceptibility and robustness, a hybrid image watermarking algorithm based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed using the characteristics of human visual system model for copyright protection and authenticity. In the proposed watermarking algorithm, one level DWT is applied to selected image blocks to obtain four sub-bands of each block and then the U component of low frequency sub-band (LL) obtained after SVD transformation is explored under different threshold values for embedding and extracting the watermark. The experimental results show that HVS model based hybrid image watermarking scheme is imperceptible and robust against several image processing operations like JPEG compression, median filtering, sharpening, cropping and addition of Gaussian noise. Peak signal to noise ratio (PSNR) and bit correction rate (BCR) are used to measure the quality of watermarked image and extracted watermark respectively.

LWT- QR decomposition based robust and efficient image watermarking scheme using Lagrangian SVR

In this paper, an efficient and robust image watermarking scheme based on lifting wavelet transform (LWT) and QR decomposition using Lagrangian support vector regression (LSVR) is presented. After performing one level decomposition of host image using LWT, the low frequency subband is divided into 4 × 4 non-overlapping blocks. Based on the correlation property of lifting wavelet coefficients, each selected block is followed by QR decomposition. The significant element of first row of R matrix of each block is set as target to LSVR for embedding the watermark. The remaining elements (called feature vector) of upper triangular matrix R act as input to LSVR. The security of the watermark is achieved by applying Arnold transformation to original watermark to get its scrambled image. This scrambled image is embedded into the output (predicted value) of LSVR compared with the target value using optimal scaling factor to reduce the tradeoff between imperceptibility and robustness. Experimental results show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against image processing operations compared to the state-of-art techniques.

Digital Image Watermarking in DCT Domain Using Finite Newton Support Vector Regression

Finite Newton Support Vector Regression (FNSVR) is a reliable and robust method for regression analysis which unlike other support vector regression methods, converges the computation in a few iterations. In this paper, we have used FNSVR method for embedding and extracting a binary image as a watermark in 8-bit gray scale cover images. Digital image watermarking being a time consuming task, requires fast embedding and extraction procedures and overall it should have a low time complexity. FNSVR being an algorithm which consumes less number of iterations, embeds a given watermark in a short time span. Computed PSNR values indicate good quality of the signed images. parameter is computed and its values indicate that the extraction process is quite successful.

General regression neural network based image watermarking scheme using fractional DCT-II transform

A novel gray scale image watermarking scheme in frequency domain is proposed through the combination of image features, extracted using fractional discrete Cosine transform (DFrCT) with general regression neural network (GRNN). The watermark is a binary image which is embedded into the output obtained by trained GRNN based on the relationship between the low frequency (LF) DFrCT coefficient and its neighborhood of each selected block according to human visual system criteria. Due to better function approximation, learning and generalization capability of GRNN, extraction of watermark using trained neural network is quite successful. The transform order of fractional discrete cosine transform provides the security to the proposed scheme. Experimental results prove that the proposed image watermarking scheme is imperceptible as quantified by high peak signal to noise ratio (PSNR) and robust as measured by the bit correct ratio between the original watermark and extracted watermark.

Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking

A novel imperceptible, secure and robust grayscale image watermarking scheme using Lagrangian twin support vector regression (LTSVR) and genetic algorithm (GA) in discrete Cosine transform (DCT) domain is presented in this manuscript. Fuzzy entropy is used to select the relevant blocks for embedding the watermark. Selected number of blocks based on fuzzy entropy not only reduces the dimensionality of the watermarking problem but also discards redundant and irrelevant blocks. Significant DCT coefficients having high energy compaction property of each selected block are used to form the image dataset to train LTSVR to find the non-linear regression function between the input and target vector. The adaptive watermark strength, different for each selected block, is decided by the GA process based on well defined fitness function. Due to good learning capability of image characteristics and high generalization property of LTSVR, watermark is successfully extracted from the watermarked images against a series of image processing operations. From the experimental and comparison results performed on standard and real world images, it is inferred that the proposed method is suitable for copyright protection applications where high degree of robustness is desirable.

Adaptive Image Watermarking Scheme Using Fuzzy Entropy and GA-ELM Hybridization in DCT Domain for Copyright Protection

A novel semi-blind image watermarking scheme based on fuzzy entropy and genetic algorithm (GA)—extreme learning machine (ELM) hybridization in discrete Cosine transform (DCT) domain for copyright protection is proposed in this paper. The selection of non overlapping blocks to embed the binary watermark is based on fuzzy entropy. As fuzzy entropy is able to discriminate data distribution under noise corrupted and redundant condition, feature extraction is more robust against various attacks. Each selected block is followed by 2-D DCT to transform it from spatial to frequency domain. Low frequency coefficients have good energy compactness and are robust to image processing attacks. As addition of noise corresponds to high frequency coefficients, these are not considered to embed the watermark in the proposed approach. The optimal scaling factor used to control the strength of watermark for each selected block of the image based on its noise sensitivity and tolerance limit is determined using GA optimization process. ELM is used to find an optimal regression function between the input feature vector (low frequency DCT coefficients) and corresponding target vector (in which the watermark bits are embedded) of each selected block. Then watermark embedding and extraction is performed intelligently by the regression function obtained by the trained ELM. The experimental results show that the proposed scheme is highly imperceptible and robust to geometric and non geometric attacks such as JPEG compression, filtering, noise addition, sharpening, gamma correction, scaling and cropping etc. To demonstrate the effectiveness of the proposed scheme, comparison with the state-of-art techniques clearly exhibits its applications for copyright protection.

Lagrangian support vector regression based image watermarking in wavelet domain

To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) and machine learning algorithms such as support vector regression (SVR), extreme learning machine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.

Gray scale image watermarking using fuzzy entropy and Lagrangian twin SVR in DCT domain

In this paper, the effect of low, middle and high frequency DCT coefficients are investigated onto gray scale image watermarking in terms of imperceptibility and robustness. The performance of Lagrangian twin support vector regression (LTSVR), which was successfully applied on synthetic datasets obtained from UCI repository for various kinds of regression problems by Balasundaram et al. [9], onto image watermarking problem, is validated by embedding and extracting the watermark on different standard and real world images. Also the good learning capability of image characteristics provides the good imperceptibility of the watermark and robustness against several kinds of image processing attacks verifies the high generalization performance of LTSVR. Through the experimental results, it is observed that significant amount of imperceptibility and robustness is achieved using low frequency (LF) DCT coefficients as compared to middle frequency (MF) and high frequency (HF) DCT coefficients as well as state-of-art technique.