Meaningful Image Research Articles

Exploiting compressed sensing and polynomial-based progressive secret image sharing for visually secure image selection encryption with authentication

Image encryption is an effective method to protect private images by converting them into meaningless ones, but many current image encryption algorithms still have security and efficiency problems. This paper combines compressed sensing (CS) and secret image sharing (SIS) to propose a certifiable visually secure image selection encryption. Firstly, a selective encryption based on multi-task convolutional neural network (SE-MTCNN) is presented to distinguish and encrypt the sensitive and non-sensitive information of plain images. Subsequently, the SIS algorithm is used to decompose the resulting plain image into multiple shadow images to disperse the risk of cryptographic image loss and resist hacking attacks. Herein, a pseudo-convolutional sliding scrambling based on chaotic sequences driven by plaintext (PCSS-CSDP) is proposed to eliminate the correlation between adjacent image pixels. Finally, the IWT-M-embedding algorithm is used to embed shadow images into carrier images to get multiple visually meaningful cipher images. Additionally, a new authentication scheme based on bit-level cyclic shifts and dynamic combination (AS-BCSDC) is introduced, and the obtained authentication information is fused into cipher images to prevent the deception of illegal users and malicious tampering of cloud platform. Experiments demonstrate the effectiveness of our algorithm, and it may be applied for image secure communication.

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

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

Visually meaningful image encryption scheme based on new-designed chaotic map and random scrambling diffusion strategy

Since most of the existing image encryption algorithms hide the plaintext information by encrypting the image into a noise like image, the encrypted image is very different from the original image, and the visual security of the encrypted image is ignored. In view of this defect, a visually meaningful image encryption scheme combining new chaotic mapping and parallel compressed sensing is proposed, which makes the image content and vision get double protection. Firstly, a newly designed one-dimensional chaotic map derived from the fractional form of trigonometric function is used for constructing the controlled measurement matrix and disturb the strong correlation of images. Secondly, a bidirectional random scrambling algorithm based on chaotic magic transformation (CMT) is designed to disturb the wavelet packet coefficient matrix of plain image. Then, the scrambled secret image is encrypted and compressed with the help of chaotic measurement matrix and the newly designed random chaotic pixel diffusion strategy. Finally, the secret image is hidden in another large carrier image such as plain image through the wavelet packet transform embedding algorithm. As a result, an image is protected by both visual and information security. Simulation results and security analysis make clear that the proposed encryption scheme is feasible and effective under the premise of guaranteeing visual security and decryption quality.

Meaningful Secret Image Sharing with Uniform Image Quality

In meaningful secret image sharing (MSIS), a secret image is divided into n shadows. Each shadow is meaningful and similar to the corresponding cover image. Meaningful shadows can reduce the suspicion of attackers in transmission and facilitate shadow management. Previous MSIS schemes always include pixel expansion, and cross-interference from different shadows may exist when cover images are extremely unnatural images with large black and white blocks. In this article, we propose an MSIS with uniform image quality. A threshold t is set to determine the absolute salient regions. More identical bits are allocated according to saliency values in the absolute saliency region, which can improve image quality. In addition, the new identical bits allocation strategy also adjusts the randomness of the shadow images, generating shadows with uniform image quality and avoiding the cross-interference between different shadows. Experimental results show the effectiveness of our proposed scheme.

High-quality visually secure image cryptosystem using improved Chebyshev map and 2D compressive sensing model

In this paper, we propose an improved Chebyshev map and a visually meaningful image encryption (VMIE) algorithm based on two-dimensional compressive sensing (2DCS). First, the adoption of 2DCS compresses the plain image, wherein the measurement matrix is processed by the simulated annealing algorithm to improve the quality of the reconstructed image. Second, a diffusion operation is performed on the sample data. Furthermore, a matrix encoding matrix is employed to insert the cryptographic image into the host image to obtain a visually meaningful steganographic image. The experimental results and comprehensive analyses show that the proposed scheme has feasible performance and stands up to various attacks. Eventually, the comparison with existing related schemes demonstrates that the proposed image encryption system has the advantages of visual security and reconstruction quality.

CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Encryption of meaningful images into unidentifiable noise-like images can improve the security of images during storage and transmission. In this paper, a novel color image encryption method based on the lifting scheme and cross-component permutation (CIE-LSCP) is introduced. First, original image is divided into red, green and blue components, and then the three components are processed by a preprocessing strategy based on the lifting scheme (PSLS) to change the statistical distribution of the pixels. Second, a block-based cross-component permutation based on index vectors (BCPIV) is presented to divide three preprocessed components into blocks and perform the cross-component permutation operation on them, and three random matrices are utilized to determine the target component, target block and target pixel position of the current pixel to be moved, respectively. Subsequently, a multi stochastic diffusion based on random sequences (MSDRS) is developed to alter the pixel values of the permutated components, and finally a ciphertext image is gotten by merging the three components. Moreover, the SHA256 hash values of the plaintext image are used to obtain the initial parameters of the chaotic system, and the obtained chaotic sequences are applied in the image encryption process. Wherein the generated random sequences are highly reliant on the plaintext image, making the encryption scheme resistant to both known-plaintext attacks and chosen-plaintext attacks. Experimental results demonstrate that the proposed scheme has good security and effectiveness and can be applied for secure transmission of digital images over the Internet.

Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses.

Primitively visually meaningful image encryption: A new paradigm

Traditional image encryption schemes turn images with rich visual contents into meaningless noise-like ones. This assures privacy security at the expense of visual meaning. To cope with this issue, an encryption method called visually meaningful image encryption (VMIE), by which the encrypted image can have visual meaning, is proposed to protect image privacy and visual observability. This visual meaning, however, is unrelated to the original image, which, like traditional encryption, compromises the primitively visual meaning. Motivated by this, we investigate the term ‘visual meaning’ in depth and suggest a new VMIE paradigm, namely, primitively visually meaningful image encryption, as well as accompanying goals. A novel permutation-diffusion architecture is specifically designed to match the paradigm, which can preserve the primitively visual meaning in the encrypted image. Furthermore, the prototype of this paradigm is constructed using a chaotic system and DNA coding. It is then applied to do experiments to demonstrate the superiority of this work. It should be observed that the primitively visual meaning is preserved and of good quality in the encrypted image.

A novel visually meaningful image encryption algorithm based on parallel compressive sensing and adaptive embedding

Compared with traditional image encryption algorithms that generate noise-like images, visually meaningful image encryption achieves the dual protection of digital images in content and vision, but the embedded position is often fixed or controlled by keys. If the embedding position is not appropriate, the effect of encryption and decryption will be affected to a certain extent. In this paper, a novel visually meaningful image encryption and adaptive embedding scheme is proposed by using chaotic cellular neural network (CCNN), parallel compressive sensing (PCS), and least significant bit (LSB) embedding in transform domain. First, 2D discrete wavelet transform (DWT) is used to sparse the plain image. Then, the sparse matrix after threshold processing is encrypted and measured by local binary pattern (LBP) and PCS technology. Finally, the information entropy is used to analyze the texture degree of the carrier image for adaptive embedding, so as to obtain the visually meaningful cipher image. Moreover, the ability of the algorithm to resist known- and chosen- plaintext attacks is improved by using the plaintext eigenvalue as part of the encryption key stream. Considering the practicability of the scheme, the plaintext eigenvalue is hidden in the visually meaningful cipher image, which reduces unnecessary key transmission. Experimental results show that the scheme is effective on the premise of visual security and decryption quality.

An efficient double-image encryption and hiding algorithm using a newly designed chaotic system and parallel compressive sensing

In this paper, an efficient double-image visually meaningful encryption algorithm combining a new chaotic system, parallel compressive sensing (PCS) and fast Fourier transform (FFT), is proposed. First, Arnold scrambling is performed on two plain images after wavelet transform. Then, the scrambled secret images are compressed by applying a key-controlled partial Hadamard matrix. Finally, the final cipher image with the same resolution as the plain image is acquired by embedding the two encrypted and measured secret images into a carrier image through FFT. To achieve a higher level of security, a new chaotic system combining infinite collapses and a tent map (ICTM) is introduced for disturbing the strong correlation of images and randomly embedding encrypted data into carrier images. The parameters of ICTM are generated by the hash value of the plain images, which enhances the ability of the cryptographic system to resist plaintext attacks. Moreover, the parameters related to the plaintext are embedded into the alpha channel of the visually meaningful cipher image to reduce unnecessary key transmission. The simulation results and performance analysis show that the cryptosystem is feasible and efficient under the condition of guaranteeing all the evaluation indices.

Optical image encryption scheme based on quantum s-box and meaningful ciphertext generation algorithm

With the development of quantum computers, difficult computational problems of classical cryptosystems could potentially become solvable. Hence, due to the powerful computing power of quantum computers, all current cryptographic algorithms based on computational complexity may become ineffective. Furthermore, most of the ciphertexts generated by traditional encryption schemes are noise-like, and such ciphertexts are easily suspected by cryptanalysts during transmission. To solve these problems, a novel image encryption scheme is proposed by visually meaningful ciphertext generation and quantum walks (QWs). The proposed scheme is composed of three phases: (I) Permutation, (II) Diffusion, and (III) Visually meaningful ciphertext generation. In phase I, the quantum substitution box (s-box) is firstly constructed by QWs, and the original image is permutated by the quantum s-box to reduce the correlation between adjacent pixels. In phase II, the permutated quantum image is diffused by a key image, which is constructed by the probability matrix generated by the same QWs. In phase III, the diffused image is disguised as a meaningful image to avoid the suspicions of cryptanalysts. Compared to traditional encryption algorithms, the introduction of quantum technology effectively improves the security of the cryptosystem. Compared to previous quantum encryption methods, our algorithm generates meaningful ciphertexts that can further enhance security by preventing cryptanalysts from suspecting noise-like ciphertexts. Thus, our encryption scheme is secure even in the future quantum computer age. Besides, the decryption time of our proposed scheme can also be effectively reduced since the decryption process can be optically implemented, partially. Therefore, real-time scenarios can be satisfied. Simulation results and numerical analysis show the effectiveness of the proposed scheme.

Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.

A visually secure image encryption scheme using adaptive-thresholding sparsification compression sensing model and newly-designed memristive chaotic map

Aiming at the deficiencies of existing image compression-encryption algorithms, such as unstable reconstruction performance and weak crypticity, a novel visually secure image encryption scheme based on the adaptive-thresholding sparsification compression sensing model and the newly designed memristive chaotic map is proposed in this paper. First, the plain image is sparsely represented in the wavelet packet domain. Then, the coefficient matrix is scrambled and compressed using parallel compressive sensing with assistance from the newly developed adaptive-thresholding sparsification model. Next, the compressed image is re-encrypted by the FAN transform and diffusion to generate a noise-like secret image. Finally, the reversible matrix encoding-based embedding method is employed to create a meaningful cipher image. To improve the execution efficiency of the encryption algorithm without affecting the security performance, a low-dimensional discrete memristive chaotic map is designed and applied in the compression, encryption and embedding stages. Additionally, the Lp matrix norm is utilized to establish an adaptive threshold model to effectively enhance the compression performance. Simulation results and analyses are presented to demonstrate the effectiveness, the compressibility, and the secrecy of the proposed scheme.

A Probabilistic Chaotic Image Encryption Scheme

This paper proposes a probabilistic image encryption scheme that improves on existing deterministic schemes by using a chaining mode of chaotic maps in a permutation-masking process. Despite its simplicity, the permutation phase destroys any correlation between adjacent pixel values in a meaningful image. The masking phase, however, modifies the pixel values of the image at hand using pseudorandom numbers with some other initiated random numbers so that any slight change in the plain image spreads throughout the corresponding cipher image. These random numbers ensure the generation of distinct cipher images for the same plain image encryption, even if it is encrypted multiple times with the same key, thereby adding some security features. Simulations show that the proposed scheme is robust to common statistical and security threats. Furthermore, the scheme is shown to be competitive with existing image encryption schemes.

THE FIRE IN THE STRUCTURE OF THE DYNAMIC VISUAL COMMUNICATION: THE METHODS OF IMAGE CREATION

The modern person lives in the constant flow of visual messages and images that make their identification and perception more complicated. Against this background the actual issue is to search the ways of focusing on the certain information and attracting people’s attention due to the outrageous images creation. One of such ways is the implementation of the natural fire element into the design objects structure. The live fire as the contradictory meaningful image of the destruction and creation becomes the innovative means of visual language that activates the sensitive experience of the world cognition. In this regard, such huge changes in the visual communication design require the system analysis of the theory and practice of the integrated design systems, which are based on the physico-chemical properties of natural elements, especially fire.

A visually meaningful image encryption algorithm based on adaptive 2D compressive sensing and chaotic system

A visually meaningful image encryption algorithm based on adaptive 2D compressive sensing and chaotic system

Controllable and Identity-Aware Facial Attribute Transformation.

Modifying facial attributes without the paired dataset proves to be a challenging task. Previously, approaches either required supervision from a ground-truth transformed image or required training a separate model for mapping every pair of attributes. These limit the scalability of the models to accommodate a larger set of attributes since the number of models that we need to train grows exponentially large. Another major drawback of the previous approaches is the unintentional gain of the identity of the person as they transform the facial attributes. We propose a method that allows for controllable and identity-aware transformations across multiple facial attributes using only a single model. Our approach is to train a generative adversarial network (GAN) with a multitask conditional discriminator that recognizes the identity of the face, distinguishes real images from fake, as well as identifies facial attributes present in an image. This guides the generator into producing an output that is realistic while preserving the person's identity and facial attributes. Through this framework, our model also learns meaningful image representations in a lower dimensional latent space and semantically associate separate parts of the encoded vector with both the person's identity and facial attributes. This opens up the possibility of generating new faces and other transformations such as making the face thinner or chubbier. Furthermore, our model only encodes the image once and allows for multiple transformations using the encoded vector. This allows for faster transformations since it does not need to reprocess the entire image for every transformation. We show the effectiveness of our proposed method through both qualitative and quantitative evaluations, such as ablative studies, visual inspection, and face verification. Competitive results are achieved compared to the main competition (CycleGAN), however, at great space and extensibility gain by using a single model.

Data Concealment Using Steganography Technique

Abstract: Visual secret sharing (VSS) systems hide hidden images in shares that are also published on clarity or decrypted and saved in digital form. The shares can seem as noise-like pixels or as meaningful images, but this will raise suspicion and increase the risk of interception during transmission. As a result, VSS schemes face a transmission danger problem for both the secret and the individuals involved in the VSS system. To solve this issue, we presented a new palette-grounded steganography technique that uses a texture with LSB, as well as a natural-image-grounded VSS scheme (NVSS scheme) that shares secret images via colored carrier media to hide the secret and the actors during the transmission phase. To conceal secret messages, we convert the texture conflation process into steganography. Rather than using a being cover image to conceal dispatches, our algorithm conceals the source texture image and embeds hidden dispatches during the printing process. Prints or hand-painted filmland in digital or published form can be used for the natural shares. We also propose possible methods for concealing the secret in order to reduce the transmission threat problem for the share. The experimental results show that the proposed approach is an excellent solution to the transmission threat problem for VSS schemes. Keywords: visual secret sharing (VSS), steganography, natural-image-based VSS scheme (NVSS scheme), OR Code, Palette Based Steganography

Verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL)

Image encryption converts the plain image into a noise-like one to protect information security. However, the obtained cipher images are easy to attract the attention of third parties during transmission and storage, and it is vulnerable to information damage and tampering caused by hacking or noise pollution, which in turn makes the receiver unable to obtain the correct decrypted image even spending many resources. To address these issues, this paper proposed a verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL). Specifically, the plain image is preprocessed and compressed to obtain compressed image via CS, achieving the purpose of reducing the image size. Subsequently, to improve the encryption effect, dynamic cyclic shift confusion method (DCSCM) and diffusion method based on improved game of life (DMIGOL) are presented and performed on the compressed image to obtain the secret image. Next, multiple shadow images are obtained using the secret image sharing based on the Chinese remainder theorem (CRT-SIS) on the secret image, which improves the robustness of this scheme and achieves multi-party data transfer of image. Finally, the shadow images are embedded into the carrier images respectively to obtain visually meaningful cipher images, reducing the attacker's attention to the cipher images during transmission. Additionally, the Hamming distance authentication method relying on plain image and carrier image is applied, and users can perform identity authentication and integrity check on cipher image. Experimental results demonstrate the security and effectiveness of the proposed scheme.

A Novel Image Denoising and Segmentation Using Machine Learning with SRM Strategy

Objectives: In the last decade, artificial intelligence (AI) and machine learning (ML) have a significant impact on image analysis and segmentation. The most demanding aspect in digital image processing is efficient segmentation to extract the desired features or recognize the hidden patterns of the digital images, which are one of the major challenges. Even in existing segmentation techniques, due to under segmentation issues, particularly in remote or arial image analysis, necessary object features and residual details are not properly segmented. Therefore, the study presents a new method for image segmentation using machine learning, which addresses these limitations. Methods: This article presents a novel approach for arial image denoising and segmentation using unsupervised learning. The Discrete Wavelet Transform (DWT) is primarily used as a pre-processing tool, while edge details of the decomposed image are automatically preserved with the help of machine understanding. The use of a fast Non-Local (NL)-means filter provides a better visual effect for low-frequency image features, and the filtered image is partitioned into the number of clusters by Minimum- Spanning Tree (MST) clustering algorithm. Further, Statistical Region Merging (SRM) is used to eliminate the unwanted regions of the clustered image to give meaningful image details. Finally, the segmented image is projected at the wavelet restoration level. Here, the standard images are extracted from the SIPI database, and the system does not require training prototypes but works in an ”unsupervised” way. Findings: The proposed system segment 1014 regions in the first phase and the segmented image features efficiently recognize the hidden patterns of the arial image (7.1.06). However, to track the required object features, statistical Region Merging (SRM) is utilized, a drastic variation in the merging process eliminates the annoying details and brings effective outcomes, which involves 29 essential features with retained residual details at level-2 decomposition and is compared with leading segmentation techniques. The denoising performance of the suggested study provides a 21.73 Peak Signal to Noise Ratio (PSNR) value in 17.96 elapsed time. simulation results have proven to produce better segmentation and denoising with less iteration time. A qualitative evaluation through visual examination justifies the proposed study and is superior to some of the popular methods. Novelty/Applications: The utilization of wavelet transform to preserve edge features during segmentation is one of the key features of this method, and it significantly overcomes the under segmentation patterns. The combination of minimum-spanning tree clustering (MST) and statistical region merging (SRM) is the major strategic step in segmenting and detecting the essential object features of arial images. Keywords: DWT; Fast NLmeans filter; Wavelet denoising; MST; SRM