De-Noising of Binary Image Using Accelerated Local Median-Filtering Approach

In the last few decades huge amounts and diversified work has been witnessed in the domain of de-noising of binary images through the evolution of the classical techniques. These principally include analytical techniques and approaches. Although the scheme was working well, the principal drawback of these classical and analytical techniques are that the information regarding the noise characteristics is essential beforehand. In addition to that, time complexity of analytical works amounts to beyond practical applicability. Consequently, most of the recent works are based on heuristic-based techniques conceding to approximate solutions rather than the best ones. In this chapter, the authors propose a solution using an iterative neural network that applies iterative spatial filtering technology with critically varied size of the computation window. With critical variation of the window size, the authors are able to show noted acceleration in the filtering approach (i.e., obtaining better quality filtration with lesser number of iterations).

Commutative Watermarking-Encryption of Multimedia Data Based on Histograms

Histogram-based watermarking schemes are invariant to pixel permutations and can thus be combined with permutation-based ciphers to form a commutative watermarking-encryption scheme. In this chapter, the authors demonstrate the feasibility of this approach for audio data and still image data. Typical histogram-based watermarking schemes based on comparison of histogram bins are prone to desynchronization attacks, where the whole histogram is shifted by a certain amount. These kind of attacks can be avoided by synchronizing the embedding and detection processes, using the mean of the histogram as a calibration point. The resulting watermarking scheme is resistant to three common types of shifts of the histogram, while the advantages of previous histogram-based schemes, especially commutativity of watermarking and permutation-based encryption, are preserved. The authors also report on the results of testing robustness of the still image watermark against JPEG and JPEG2000 compression and on the possibility of using histogram-based watermarks for authenticating the content of an image.

Reversible Data Hiding

The chapter presents an application of reversible data hiding for the authentication of image travelling over a hostile and insecure communication channel. The reversible data resides in the image and tracks any changes done to it on a communication channel. The extraction of data and any modification to its structure reveals changes in the image. This allows the use of data hiding for forensic purpose. The reversible data hiding provides an additional advantage along with active forensics. The image regains original form after removal of the embedded data. However, reversible data hiding is an interplay between the image quality and watermarking capacity. The chapter presents the generic framework for data hiding and discusses its special case reversible data hiding. It presents capacity-behavior analysis of the difference expansion scheme. It performs in-depth analysis on the type of predictor and its impact on the capacity of the reversible data hiding scheme. Finally, the chapter presents a case study to showcase the use of reversible data hiding for image authentication.

Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms

Nature-inspired metaheuristic algorithms find near optimum solutions in a fast and efficient manner when used in a complex problem like finding optimum number of neurons in hidden layers of a multi-layer perceptron (MLP). In this chapter, a classification work is discussed of malignant melanoma, which is a type of lethal skin cancer. The classification accuracy is more than 91% with visually imperceptible features using MLP. The results found are comparably better than the related work found in the literature. Finally, the performance of two metaheuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) are compared and analyzed with different parameters to show their searching nature in the two-dimensional search space of hidden layer neurons.

Analysis of Human Gait for Designing a Recognition and Classification System

Identification and recognition of a human subject by monitoring a video/image by using various biometric features such as fingerprints, retina/iris scans, palm prints have been of interest to researches. In this chapter, an attempt has been made to recognize a human subject uniquely by monitoring his/her gait. This has been done by analyzing sampled frames of a video sequence to first detect the presence of a human form and then extract the silhouette of the subject in question. The extracted silhouette is then used to find the skeleton from it. The skeleton contains a set of points that retains the connectivity of the form and maintains the geometric properties of the silhouette. From the skeleton, a novel method has been proposed involving the neighborhood of interest pixels to identify the end points representing the heel, toe, etc. These points finally lead to the calculation of gait attributes. The extracted attributes represented in the form of a pattern vector are matched using cosine distance with features stored in the database resulting in identification/rejection.

Multimedia Data Security With Recent Trends and Technologies

This chapter throws light on major multimedia data security techniques. It has become so essential in today's society which uses multimedia data in almost all walks of life. There are a lot of multimedia data transactions carried out every day. Thus, to ensure data security the techniques, steganography and watermarking are frequently used. In steganography and watermarking techniques, the data to be hidden is encrypted and fed into a transparent layer like documents, images, and it is decrypted at the receiving end by the recipient. Generally, steganographic communications are one-to-one while watermarking is one-to-many. Besides these techniques, certain other techniques are also used in the application for providing security to multimedia data. From this chapter, a detailed content about steganography and watermarking can be obtained.

Implementation of a Hybrid Classification Method for Diabetes

This chapter presents a best classification of diabetes. The proposed approach work consists in two stages. In the first stage the Pima Indian diabetes dataset is obtained from the UCI repository of machine learning databases. In the second stage, the authors have performed the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. Then they applied PSO_SVM as a feature selection technique followed by the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. In this chapter, the optimization of SVM using PSO reduces the number of attributes, and hence, applying fuzzy decision tree improves the accuracy of detecting diabetes. The hybrid combinatorial method of feature selection and classification needs to be done so that the system applied is used for the classification of diabetes.

On the Implementation of a Fragile Medical Image Watermarking Technique Using Saliency and Phase Congruency

The recent developments of enormous computer networks have invoked insecurities related to copyright theft of digital media. To be precise, the virtual sharing of medical images over networks with a novel desire of improved medical diagnosis has led to the tampering of sensitive patient identity information. In this chapter, the authors have exemplified the need of watermarking with fragile medical image watermarking using saliency and phase congruency. Initially, the saliency and phase congruency methodologies are applied on the original medical image to highlight the object features. Based on the feature map, a mask is generated which segregates the area of interest from the portions containing visual medical information. An encrypted text, containing identity of the patient, is embedded into the area of interest of the image. The results of imperceptibility and fragility criteria are satisfactory towards the implementation of a fragile watermark as the extracted watermark is found to be corrupted upon unfaithful image processing modifications.

Implementation of a Reversible Watermarking Technique for Medical Images

Electronic health records (EHR) contain patients' medical as well as personal details. With the increased use of digital media, these data are stored and transferred through the electronic media all over the world. This makes it vulnerable to unauthorized people. Digital image watermarking can be a useful process of protecting these data from attacker but causes severe and unrecoverable damage to cover media. In the case of highly sensitive images like medical images, this might creates a problem during further diagnosis. In this chapter, a reversible data hiding algorithm is proposed which also is capable of holding a large chunk of data without affecting the cover media. The main cover image is first reconstructed and hidden behind a bigger media and then the extra pixels are used to hide encrypted forms of EHR data along with an authentication signature. As EHR data and the digital signature is passed through various encryption stages while encoding, it is made more secure. The algorithm is developed on the spatial domain adding some cautious measures which made it fragile as well.

Exploring Recent Advances in Digital Video Steganography and Future Scope

In this digital era, with the advent of technology like 4G network and VOIP, video calling is the most cost effective and cutting-edge communication technique. Simultaneously, video sharing through social networking sites is very popular, as it can reach a wider public domain in seconds. This enormous use of video motivates the fact that as digital medium video can be effectively utilized for secret sharing. Using video steganography, any kind of secret data like text, image, audio, even a short video can be hidden inside another video object, which can be securely transmitted to the recipient over the internet. In this chapter, an effort has been made to relate various techniques of video steganography under a single header to identify future scope of research. Also, all possible quality metrics for videos and for measuring robustness have been studied, and different steganalysis attacks on video have been analyzed. The broad mission of this chapter is to be a quick reference to future researchers of video steganography.