The usage of images in different fields has increased dramatically, especially in medical image analysis and social media. Many risks can threaten the integrity and confidentiality of digital images transmitted through the internet. As such, the preservation of the contents of these images is of the utmost importance for sensitive healthcare systems. In this paper, the researchers propose a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. It employs a visual cryptography-based watermarking approach to provide the capabilities of forgery detection and localization. In this watermarking scheme, features and key and secret shares are generated. The feature share is constructed by extracting features from equal-sized blocks of the image by using a Walsh transform, a local binary pattern and a discrete wavelet transform. Then, the key share is generated randomly from each image block, and the secret share is constructed by applying the XOR operation between the watermark, feature share and key share. The CASIA V 1.0 and SIPI datasets were used to check the performance and robustness of the proposed method. The experimental results from these datasets revealed that the percentages of the precision, recall and F1 score classification indicators were approximately 97% for these indicators, while the percentages of the TAF and NC image quality indicators were approximately 97% and 96% after applying several known image processing and geometric attacks. Furthermore, the comparative experimental results with the state-of-art approaches proved the robustness and noticeable improvement in the proposed approach for the detection and localization of image forgeries in terms of classification and quality measures.

https://www.mdpi.com/2079-9292/11/1/136/pdf?version=1641116514

A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

/pdf/towards-secure-big-data-analysis-via-fully-homomorphic-25nxghvd.pdf

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.

/pdf/double-layer-security-using-crypto-stego-techniques-a-dgbtbyjuqb.pdf

Double layer security using crypto-stego techniques: a comprehensive review.

An efficient encoding based watermarking technique for tamper detection and localization

The advancement of the Internet of Things (IoT) has transfigured the overlay of the physical world by superimposing digital information in various sectors, including smart cities, industry, healthcare, etc. Among the various shared information, visual data are an insensible part of smart cities, especially in healthcare. As a result, visual-IoT research is gathering momentum. In visual-IoT, visual sensors, such as cameras, collect critical multimedia information about industries, healthcare, shopping, autonomous vehicles, crowd management, etc. In healthcare, patient-related data are captured and then transmitted via insecure transmission lines. The security of this data are of paramount importance. Besides the fact that visual data requires a large bandwidth, the gap between communication and computation is an additional challenge for visual IoT system development. In this paper, we present SVIoT, a Secure Visual-IoT framework, which addresses the issues of both data security and resource constraints in IoT-based healthcare. This was achieved by proposing a novel reversible data hiding (RDH) scheme based on One Dimensional Neighborhood Mean Interpolation (ODNMI). The use of ODNMI reduces the computational complexity and storage/bandwidth requirements by 50 percent. We upscaled the original image from M × N to M ± 2N, dissimilar to conventional interpolation methods, wherein images are upscaled to 2M × 2N. We made use of an innovative mechanism, Left Data Shifting (LDS), before embedding data in the cover image. Before embedding the data, we encrypted it using an AES-128 encryption algorithm to offer additional security. The use of LDS ensures better perceptual quality at a relatively high payload. We achieved an average PSNR of 43 dB for a payload of 1.5 bpp (bits per pixel). In addition, we embedded a fragile watermark in the cover image to ensure authentication of the received content.

/pdf/sviot-a-secure-visual-iot-framework-for-smart-healthcare-f0z5gcie.pdf

SVIoT: A Secure Visual-IoT Framework for Smart Healthcare

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems.

Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Hash-based image watermarking technique for tamper detection and localization

Reversible data hiding (RDH) technique allows embedding of data inside an image and later can be retrieved with the original host image after extraction. In this paper, a RDH technique using histogram bin shifting (HBS) for color images has been proposed. In the proposed technique the color image is separated into three planes and each plane is divided into four blocks and a secret data is embedded into all four blocks of all the planes of the color image by using reversible histogram bin shifting. The same algorithm has been implemented on both the color image components separately (YCbCr/RGB). The proposed scheme attains 69396 and 34478 average embedding capacity for YCbCr and RGB color image components with minimum image distortion.

Muzamil Hussan

Papers

Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Hash-based image watermarking technique for tamper detection and localization

Realization of a Reversible Data Hiding Framework using Histogram Bin Shifting for Color Images

Double layer security using crypto-stego techniques: a comprehensive review.

Self-embedding framework for tamper detection and restoration of color images