Defending Multimedia Content Embedded in Online Social Networks (OSNs) Using Digital Watermarking

In global internet usage, increasing multimedia message, which includes video, audio, images, and text documents, on the web raised a lot of consequences related to copyright. For copyright protection, authentication purpose and forgery detection digital watermarking is the robust way in social network content protection. In this technique, the privacy information is embedded inside the multimedia content like image and video. The protected content embedded inside multimedia content is called watermark-enabled information. To make more effective the process of watermarking, the content encrypted before embedding to the image. Basically, the digital watermarking embedded process implemented in two different domains called spatial and frequency domain. In spatial domain digital watermarking, the watermark information is embedded in the least significant bit of the original image on the basis of bit plane selected and on the basis of the pixels of image, embedding, and detection is performed.

Anonymous Authentication for Privacy Preserving of Multimedia Data in the Cloud

Cloud computing technology provides cost-saving and flexibility of services for users. With the explosion of multimedia data, more and more data owners would outsource their personal multimedia data on the cloud. In the meantime, some computationally expensive tasks are also undertaken by cloud servers. However, the outsourced multimedia data and its applications may reveal the data owner's private information because the data owners lose control of their data. Recently, this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data. Anonymous Authentication Scheme will be proposed in this chapter as the most relatable, applicable, and appropriate techniques to be adopted by the cloud computing professionals for the eradication of risks that have been associated with the risks and challenges of privacy.

Robust Multimedia Watermarking

The data transmission on network channel has increased tremendously in the past few decades. Watermarking is a process to add watermark as a digital signal, label, or tag into a cover media. The primary requirements of multimedia watermarking are robustness and embedding capability. The robustness is defined as the strength of an algorithm to repel the noise. However, it is challenging to achieve both at the same time. The numerous characteristics of watermarking are very imperative in the multimedia watermarking system. The researchers are using watermarking schemes in various applications such as military, digital forensics, medical, and so on. Attacks of these watermarking harm or uncover the secret information carried in the watermark. Potential researchers have been presented various techniques for balancing or improving these concerns. This chapter reviews the recent multimedia watermarking techniques on the basis of robustness and embedding capability. The characteristics, applications, and attacks on multimedia watermarking techniques are introduced.

The Cost Perspective of Password Security

This study technically analyses the maximum number of combinations for common passwords up to 12 characters long. A maximum storage size necessary for the creation of a data base that holds all possible passwords up to 12 characters is also presented along with a comparison against the publicized cost of storage from popular cloud storage providers and the national budget for intelligence and defense activities of a nation. Authors prove that it is technically possible that any password could be computed within seconds with nothing more than currently commercially available components. The study concludes that it is possible that nation states or even combined nation states working in collaboration could or already have bought private citizens' and businesses' passwords revealing that it may already be an age where the password may not be a legitimate defense for privacy anymore.

Multiple Flames Recognition Using Deep Learning

Identifying fire flames is based on object recognition which has valuable applications in intelligent surveillance. This chapter focuses on flame recognition using deep learning and its evaluations. For achieving this goal, authors design a Multi-Flame Detection scheme (MFD) which utilises Convolutional Neural Networks (CNNs). Authors take use of TensorFlow in deep learning with an NVIDIA GPU to train an image dataset and constructed a model for flame recognition. The contributions of this book chapter are: (1) data augmentation for flame recognition, (2) model construction for deep learning, and (3) result evaluations for flame recognition using deep learning.

Design and Implementation of Visual Blockchain With Merkle Tree

Although our communities are paying extensive attention to the blockchain technology, it is still far away from realistic applications. Thus, this red-hot technology could be understood and employed for visual applications. Authors focus on online videos that collect sufficient user clicks owning to the high demand every day. When people watch the videos of TV drama episodes in an online website, they often need to exactly organise the playlists in ascending or descending order. However, video websites such as YouTube can't provide this service due to multiple reasons. This chapter creates a private blockchain for these video websites and applies Merkle tree to store the sorted videos in the chain. A sorted playlist has been created in the video website. Getting out of the box of video search so as to provide a quick video ranking solution is authors' main task. Sorting results are evaluated by using edit distance.

A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark

In this chapter, Image2vec or Video2vector are used to convert images and video clips to vectors in large multimedia database. The M-tree is an index structure that can be used for the efficient resolution of similarity queries on complex objects. M-tree can be profitably used for content-based retrieval on multimedia databases provided relevant features have been extracted from the objects. In a large multimedia database, to search for similarities such as k-NN queries and Range queries, distances from the query object to all remaining objects (images or video clips) are calculated. The calculation between query and entities in a large multimedia database is not feasible. This chapter proposes a solution to distribute the M-Tree structure on the Apache Spark framework to solve the Range Query and kNN Query problems in large multimedia database with a lot of images and video clips.

Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

USRP-Based Secure Data Transmission

With the advent increase in growth of wireless technology Orthogonal Frequency Division Multiplexing (OFDM) gains popularity in recent years. OFDM forms the fundamental backbone of many currently used wireless transmission standards. It is a multicarrier modulation scheme which overcomes the need for equalizer to mitigate ISI caused due to multipath propagation. In this work, an OFDM based transceiver has been developed utilizing various modules like Source Coding, Channel Coding, IFFT-FFT, scrambler, cyclic prefix, and signal mapper. To ensure secure data transmission, Hadamard matrix is XORed with the data. This provides a low level of protection to the image which is transmitted. LabVIEW has been used as the processing tool to develop the transceiver and the prototype developed has been tested using Universal Software Radio Peripheral (USRP).

Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.