ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18-99.94dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively

Haar Transformation for Compressed Speech Hiding

علم الكتابة المغطاة هو واحد من أكثر العلوم شيوعا في مجال امنية المعلوم.  في هذا البحث ، سيتم تعديل خوارزمية لتضمين صوتك مكبوس داخل صورة رمادية باستخدام تحويل المويجات المتقطعة (Haar) . في البداية تم كبس بيانات الصوت  الى نصف حجمها الأصلي ومن  ثم تحويل البيانات المكبوسة من الترميز العشري إلى الترميز الثنائي وتضمينه داخل معاملات  الحزم الاتجاهية الاربعة (cA :Low Low ,cH :High Low ,cV:Low High,cD:High High)   الناتجة من تحليل صورة الغطاء Cover_Image باستخدام تحويل المويجة المتقطع Haar حيث ان cA   تمثل حزمة الترددات الواطئة و cH ,cV ,cD تمثل حزم الترددات العالية .         تم اختبار كفاءة الخوارزمية بقياس معاملات كفاءة الاخفاء (MSE,PSNR,SNR,Correlation) واظهرت النتائج صعوبة اكتشاف المراقب لصورة الغطاء الحاوية على البيانات السرية المطمورة.          تظهر نتائج هذا البحث أنه يمكننا بنجاح إخفاء بيانات الكلام (الصوت) في صورة رمادية ثم استخراجها مع معدل سعة  خزن  (1) خلية ثنائية (bit) لكل نقطة ضوئية   اي ان سعة الخزن باستخدام  الطريقة المقدمة يعتمد على حجم صورة الغطاء  وكذلك تبين انه معاملات الترددات العالية تكون افضل للاخفاء من حيث عدم ادراك  المتطفلين بانه يوجد بيانات سرية  داخل الوسط الحامل لها  stego_imag. Steganography  science is one of the most popular field in security direction. In this paper an algorithm will be adopted to embed a compressed speech inside a gray image using discrete wavelet (Haar transformation). In the beginning the speech was compressed up to its half original size by applying (Daubechies) then convert the speech data from decimal code to binary code and embed it inside Haar coefficients of the cover _image using the Four sub bands (cA : Low Low,cH: High Low,cV:Low High,cD: High High) which got by applying the wavelet on the cover_ image. Measuring Peak Signal to Noise Ratio (PSNR) to determine the accuracy of the stego_image with respect to the original image, MSE and the correlation factors were checked show that the proposed algorithm has positive effect in field of speech hiding.The proposed  technique in this research  turned out to be able to hide  speech data (audio) in the cover image and then extract the hidden data  with  storage rate (1) bits per pixel. Hiding capacity can be achieved using this method proportionally depends on cover_image size. High frequency coefficients have also been shown to be better for data hiding in terms of perceptibility and intruders' cannot be able to recognize the cover medium (stego_image) which included secret data

The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files

Antiforensics techniques and particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice, both techniques are widely researched and developed as considered in the heart of the modern digital era but remain double edged swords standing between the privacy conscious and the criminally malicious, dependent on the severity of the methods deployed. This paper advances the automation of hidden evidence extraction in the context of audio files enabling the correlation between unprocessed evidence artefacts and extreme Steganographic and Cryptographic techniques using the Least Significant Bits extraction method (LSB). The research generates an in-depth review of current digital forensic toolkit and systems and formally address their capabilities in handling steganography-related cases, we opted for experimental research methodology in the form of quantitative analysis of the efficiency of detecting and extraction of hidden artefacts in WAV and MP3 audio files by comparing standard industry software. This work establishes an environment for the practical implementation and testing of the proposed approach and the new toolkit for extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations. The proposed multi-approach automation demonstrated a huge positive impact in terms of efficiency and accuracy and notably on large audio files (MP3 and WAV) which the forensics analysis is time-consuming and requires significant computational resources and memory. However, the proposed automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present) but overall achieve a balance between detecting hidden data accurately along with minimising the false alarms.Comment: Wires Forensics Sciences Under Revie

Reversible and imperceptible watermarking approach for ensuring the integrity and authenticity of brain MR images

The digital medical workflow has many circumstances in which the image data can be manipulated both within the secured Hospital Information Systems (HIS) and outside, as images are viewed, extracted and exchanged. This potentially grows ethical and legal concerns regarding modifying images details that are crucial in medical examinations. Digital watermarking is recognised as a robust technique for enhancing trust within medical imaging by detecting alterations applied to medical images. Despite its efficiency, digital watermarking has not been widely used in medical imaging. Existing watermarking approaches often suffer from validation of their appropriateness to medical domains. Particularly, several research gaps have been identified: (i) essential requirements for the watermarking of medical images are not well defined; (ii) no standard approach can be found in the literature to evaluate the imperceptibility of watermarked images; and (iii) no study has been conducted before to test digital watermarking in a medical imaging workflow. This research aims to investigate digital watermarking to designing, analysing and applying it to medical images to confirm manipulations can be detected and tracked. In addressing these gaps, a number of original contributions have been presented. A new reversible and imperceptible watermarking approach is presented to detect manipulations of brain Magnetic Resonance (MR) images based on Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realise a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by encoding the data into smooth regions (blocks that have least differences between their pixels values) inside the Region of Interest (ROI) part of medical images and also through the elimination of the large location map (location of pixels used for encoding the data) required at extraction to retrieve the encoded data. This compares favourably to outcomes reported under current state-of-art techniques in terms of visual image quality of watermarked images. This was also evaluated through conducting a novel visual assessment based on relative Visual Grading Analysis (relative VGA) to define a perceptual threshold in which modifications become noticeable to radiographers. The proposed approach is then integrated into medical systems to verify its validity and applicability in a real application scenario of medical imaging where medical images are generated, exchanged and archived. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible and reversible watermarking approach, that may establish increased trust in the digital medical imaging workflow

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications