Securing Fingerprint Images Through PSO Based Robust Facial Watermarking

2012 ◽  
Vol 6 (2) ◽  
pp. 34-52 ◽  
Author(s):  
Roli Bansal ◽  
Priti Sehgal ◽  
Punam Bedi
Keyword(s):  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Facial Image ◽  
Human Visual Perception ◽  
Prediction Ability ◽  
Host Image ◽  
Dct Domain ◽  
Certainty Level ◽  
Watermarking Scheme

Presented is an efficient watermarking scheme using Particle Swarm Optimization (PSO) to watermark host fingerprint images with their corresponding facial images in the Discrete Cosine Transform (DCT) domain. PSO is used to find the best DCT coefficients’ locations in the host image where the facial image data can be embedded, so that the distortion produced in the host image is minimum. The objective function for PSO is formulated in terms of the Structural Similarity Index (SSIM) and the Orientation Certainty Level Index (OCL) so as to base it on the simple visual effect of the human visual perception capability and correct minutia prediction ability. The results exhibit better watermarked image quality while retaining the feature set of the original fingerprint. Moreover, the proposed technique is robust so that the extraction of watermark is possible even after the watermarked image is exposed to attacks. As a result, at the receiver’s end, the watermarked fingerprint image and the extracted facial image can be verified for a secure and accurate biometric based personal authentication.

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Pothole Classification Model Using Edge Detection in Road Image

2020 ◽  
Vol 10 (19) ◽  
pp. 6662
Author(s):  
Ji-Won Baek ◽  
Kyungyong Chung
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Detection Algorithm ◽  
Classification Model ◽  
Specific Object ◽  
Pixel Value ◽  
Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

scholarly journals Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

2019 ◽  
Vol 8 (11) ◽  
pp. 1719-1734
Keyword(s):  
Fractional Order ◽  
Statistical Significance ◽  
Similarity Index ◽  
Optimal Solution ◽  
Image Data ◽  
Bat Algorithm ◽  
Structural Similarity ◽  
Standard Test ◽  
Data Set ◽  
Multi Level

A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.

scholarly journals Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index

Biosensors ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 504
Author(s):  
Vicky Mudeng ◽  
Minseok Kim ◽  
Se-woon Choe
Keyword(s):  
Structural Information ◽  
Optical Tomography ◽  
Similarity Index ◽  
Structural Similarity ◽  
Similarity Score ◽  
Human Visual Perception ◽  
Structural Similarity Index ◽  
Non Invasive ◽  
Numerical Assessment ◽  
Sharpness Parameter

Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties’ distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.

scholarly journals A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 975 ◽  
Author(s):  
Shaekh Hasan Shoron ◽  
Monamy Islam ◽  
Jia Uddin ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
...  
Keyword(s):  
Digital Watermarking ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Threshold Value ◽  
Discrete Wavelet ◽  
Biomedical Images ◽  
Fuzzy C Means ◽  
Biomedical Image ◽  
Watermarking Scheme

Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).

scholarly journals The fidelity of compressed and interpolated medical images

2020 ◽  
pp. 1-11
Author(s):  
Ilona Anna Urbaniak ◽  
Macin Wolter
Keyword(s):  
Medical Images ◽  
Imaging Modality ◽  
Similarity Index ◽  
Objective Measure ◽  
Image Data ◽  
Structural Similarity ◽  
Lossy Compression ◽  
Structural Similarity Index ◽  
Regulatory Bodies

Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a fullreference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.

scholarly journals A Robust Color Image Watermarking Scheme using Chaos for Copyright Protection

2019 ◽  
Vol 38 (2) ◽  
pp. 361-378
Author(s):  
Muhammad Asif Khan ◽  
Umar Ajaib Khan ◽  
Asim Ali ◽  
Fawad Hussain ◽  
Wasif Nisar
Keyword(s):  
Image Quality ◽  
Color Image ◽  
Image Watermarking ◽  
Logistic Map ◽  
Similarity Index ◽  
Structural Similarity ◽  
Quality Analysis ◽  
Color Image Watermarking ◽  
Copyright Information ◽  
Watermarking Scheme

An exponential growth in multimedia applications has led to fast adoption of digital watermarking phenomena to protect the copyright information and authentication of digital contents. A novel spatial domain symmetric color image robust watermarking scheme based on chaos is presented in this research. The watermark is generated using chaotic logistic map and optimized to improve inherent properties and to achieve robustness. The embedding is performed at 3 LSBs (Least Significant Bits) of all the three color components of the host image. The sensitivity of the chaotic watermark along with redundant embedding approach makes the entire watermarking scheme highly robust, secure and imperceptible. In this paper, various image quality analysis metrics such as homogeneity, contrast, entropy, PSNR (Peak Signal to Noise Ratio), UIQI (Universal Image Quality Index) and SSIM (Structural Similarity Index Measures) are measures to analyze proposed scheme. The proposed technique shows superior results against UIQI. Further, the watermark image with proposed scheme is tested against various image-processing attacks. The robustness of watermarked image against attacks such as cropping, filtering, adding random noises and JPEG compression, rotation, blurring, darken etc. is analyzed. The Proposed scheme shows strong results that are justified in this paper. The proposed scheme is symmetric; therefore, reversible process at extraction entails successful extraction of embedded watermark.

Secure Image Watermarking Based on LWT and SVD

2018 ◽  
Vol 18 (04) ◽  
pp. 1850021 ◽  
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Speech Signal ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Structural Similarity ◽  
Signal Transformation ◽  
Host Image ◽  
Confidential Data ◽  
Value Decomposition ◽  
Watermarking Scheme

Nowadays, digital watermarking is employed for authentication and copyright protection. In this paper, a secure image watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD), is proposed. Both LWT and SVD are used as mathematical tools for embedding watermark in the host image. In this work, the watermark is a speech signal which is segmented into shorted portions having the same length. This length is equal to 256 and these different portions constitute the different columns of a speech image. The latter is then embedded into a grayscale or color image (the host image). This procedure is performed in order to insert into an image a confidential data which is in our case a speech signal. But instead of embedding this speech signal directly into the image, we transform it into a matrix and treated it as an image (“a speech image”). Of course, this speech signal transformation permits us to use LWT-2D and SVD to both the host image and the watermark (“a speech image”). The proposed technique is applied to a number of grayscale and color images. The obtained results from peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) computations show the performance of the proposed technique. Experimental evaluation also shows that the proposed scheme is able to withstand a number of attacks such as JPEG compression, mean and median attacks. In our evaluation of the proposed technique, we used another technique of secure image watermarking based on DWT-2D and SVD.

Sign in / Sign up

Export Citation Format

Share Document