A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image.We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform.The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload.By scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration.The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system.The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker.The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical images.Various performance metrics proves that the retrieved cover image and hidden image are both intact.This System is proven to withstand rotation attack as well.

A secure image steganography algorithm based on least significant bit and integer wavelet transform

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio,or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits（LSBs） of the approximation coefficients of the integer wavelet transform（IWT） of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio（PSNR） and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error（MSE） and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation（NCC）. The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms.

Seismic data compression based on integer wavelet transform

Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in can- celing data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.

An Approach to Integer Wavelet Transform for Medical Image Compression in PACS

We study an approach to integer wavelet transform for lossless compression of medical image in medical picture archiving and communication system (PACS). By lifting scheme a reversible integer wavelet transform is generated, which has the similar features with the corresponding biorthogonal wavelet transform. Experimental results of the method based on integer wavelet transform are given to show better performance and great applicable potentiality in medical image compression.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this study.First of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public keys.To make the chaotic sequence more random,a mathematical model is constructed to improve the random performance.Then,the plain image is compressed and encrypted to obtain the secret image.Secondly,the secret image is inserted with numbers zero to extend its size same to the plain image.After applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier image.Finally,a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT.Here,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic system.Especially,information entropy of the plain image is employed to produce the initial conditions of chaotic system.As a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the communication.Experimental simulations show that the normalized correlation(NC)values between the host image and the cipher image are high.That is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.

A LOSSLESS COMPRESSION ALGORITHM OF REMOTE SENSING IMAGE FOR SPACE APPLICATIONS

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.

An Image Fusion Algorithm Based on Lifting Scheme

Taking the advantage of the lifting scheme＇s characters that can build wavelet translorms tor transforming from integer to integer and the quality of the reconstructing image＇is independent of the topology way adopted by the boundary, an image fusion algorithm based on lifting scheme is proposed. This paper discusses the fundamental theory of lifting scheme firstly and then after taking transform analysis according to a kind of images that need to be confused.

Medical image lossless compression based on combining an integer wavelet transform with DPCM

To improve the classical lossless compression of low efficiency,a method of image lossless compression with high efficiency is presented.Its theory and the algorithm implementation are introduced.The basic approach of medical image lossless compression is then briefly described.After analyzing and implementing differential plus code modulation(DPCM)in lossless compression,a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed.The analysis and simulation results show that this new method is simpler and useful.Moreover,it has high compression ratio in medical image lossless compression.

Wavelet Based Image Authentication and Recovery

In this paper, we propose a secure semi-fragile watermarking technique based on integer wavelet transform with a choice of two watermarks to be embedded. A self-recovering algorithm is employed, that hides the image digest into some wavelet subbands for detecting possible illicit object manipulation undergone in the image. The semi-fragility makes the scheme tolerant against JPEG lossy compression with the quality factor as low as 70%, and locates the tampered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced by using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees safety of a watermark, recovery of image and localization of tampered area.

A Novel Zero-Watermark Copyright Authentication Scheme Based on Lifting Wavelet and Harris Corner Detection

In this paper,a novel zero-watermark copyright authentication scheme based on Internet public certification system is proposed.This approach utilizes Haar integer wavelet transform based on a lifting scheme and adaptive Harris corner detection to extract image features,which will be used to produce a binary feature map,and the map is very crucial to the generation of watermark registered later.By properly choosing the parameters of aforementioned techniques such as the threshold T and the radius of local feature region R,the feature map is so much more stable and distinguishing that it can be used to construct robust watermark.Simulations demonstrate that the proposed scheme is resistant to many kinds of signal processing and malicious attacks such as Gaussian blurring,additive noising,JPEG lossy compression,cropping,scaling and slight rotation operation.Compared with a relative scheme such as that of Chang＇s,the scheme in this paper is more practicable and reliable and can be applied to the area of copyright protection.