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Images frequently are helpless to burglary and copyright encroachment. There are numerous events where pictures were illegally copied from websites for utilization or monetary profit and were gotten away from equity, prompting misfortunes for the battled proprietors or innovators. Techniques for securing and recognizing digital pictures and their owners from adversaries are thus required. In this paper, a watermarking procedure in hybrid domain is proposed for copyright assurance of images. The strategy inserts watermarks in Non-Subsampled Contourlet Transform (NSCT) and Redundant Discrete Wavelet Transform (RDWT) areas to accomplish better invisibility, and robustness. The blind extraction of watermark can be performed associating the arbitrarily produced PN arrangements. The experimental results demonstrate that the combination of NSCT – RDWT improves the nature of watermarked image when tried on standard and medical images. The experimental results indicated that the proposed scheme provides good imperceptibility and robustness against various kind of watermarking attacks. The main strength of proposed scheme is that it provides good imperceptibility for watermarked images up to 58 dB along with good robustness for watermark image up to 0.99 against various types of attacks and equally works for various kind of images such as greyscale and medical images. Further, the performance of proposed scheme indicated that the quality of generated watermarked medical image has fulfilled all parameters and benefits for secure telemedicine applications.

Application of fragile watermarking on biometric images stored at a server or cloud ensures proper authentication and tamper detection when access to the servers was shared. In this paper, a hybrid domain fragile watermarking technique for authenticity of color biometric images, using hybridization of various transforms such as discrete cosine transform (DCT), fast discrete curvelet transform (FDCuT), and singular value decomposition (SVD) is proposed. The hybrid transform coefficients are modified according to the scrambled color watermark to obtain watermarked color biometric image. The security of this technique is strengthened with the usage of Arnold scrambling, and by using multiple secret keys. The proposed technique is analyzed on FEI Brazilian face database. The experimental results show that this technique performs better than the existing fragile watermarking techniques.

The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.

The security of information is the major problem faced in today’s digital environment. Biometric Systems helps in achieving high–end security at precise performance requirements for various applications. This paper surveys the present multimodal and unimodal biometric based authentication systems which are in use, and their future possibilities. The authentication techniques that can identify the individual using unique features of the human such as face, fingerprint, DNA, speech and iris are discussed. Different recognition systems for new biometric traits such as voice, gait, and lip moment are also focused. This paper also focuses on information security of biometric systems and their performance requirements.

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

A lot of research on medical image watermarking has been done to fulfill the requirements of robustness and imperceptibility. Also, the security of watermark logos has been considered in many studies. In these studies, the value of weight or scaling factor is taken as user-defined or fixed. Sometimes due to the wrong selection of this value, the watermarking scheme has not given the desired results for the given medical dataset. Therefore optimization of this value is required to overcome this limitation in existing medical image watermarking schemes. Also, in the literature there are fewer watermarking schemes using bioinspired algorithms for the security of medical images. These two points motivate the development of new watermarking schemes using a bioinspired algorithm for the security of medical images. In this chapter, an intelligent watermarking scheme based on the genetic algorithm (GA) is discussed, including its different features. Also in this chapter, an image watermarking scheme using hybridization of discrete cosine transform-singular value decomposition and a GA is developed and analyzed for the security of medical images for telemedical applications. Here, GA is used for optimization of the scaling factor based on the nature of the cover medical image and watermark logo.