摘要：
The low-latency property is becoming increasingly crucial in response to the demand for data processing in the Internet of Things (IoT) environment, especially in lightweight cryptography. A low-latency lightweight block cipher called LTLBC is proposed in this paper. In particular, we propose a hybrid approach of word-wise involutive mapping and a bit-wise permutation, with careful selection of the shift and permutation parameters. This scheme enables LTLBC to achieve better diffusion than Midori and MANTIS. Also, a 4x4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4\times 4$$\end{document} S-box which is constructed through a gate-level bottom-up circuit search has good security and latency characteristics. LTLBC mainly focuses on the hardware environment of fully unrolled architecture. LTLBC achieves a minimum latency of around 4.73 ns, with a total area of only 10007.6 mu m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu m<^>2$$\end{document} based on NanGate 45nm technology. The comprehensive safety analysis shows LTLBC has sufficient redundancy rounds to resist attacks, such as differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis, etc.
摘要：
The massive collection and transmission of various crop and livestock data in smart agriculture leads to serious security concerns. Furthermore, many Internet of Things (IoT) devices in smart agriculture are battery-powered, with limited energy resources. Therefore, a low energy lightweight block cipher (LELBC) is proposed to overcome the data leakage problem during sensor data transmission in smart agriculture. Firstly, a new permutation substitution permuta-tion (PSP) structure is proposed, taking into account the energy resource constraints of unified encryption and decryption (ED) circuits. It has highly consistent encryption and decryption and a good diffusion effect. Secondly, a 4-bit low energy involutive S-box is obtained based on a genetic algorithm. The proposed S-box has lower area and latency compared to the existing S-boxes. The experimental data show that LELBC consumes 1864 gate equivalents (GE) in area and 6.99 mu J/bit in energy (encryption + decryption) under the UMC 0.18 mu m 1P6M process library. LELBC decreases energy and area consumption by 24.02% and 24.04%, respectively, compared to Midori. Finally, a temperature collection and encryption transmission platform is established. LELBC is deployed on the platform to encrypt the collected data, establishing the first line of defense for the secure transmission of smart agriculture sensor data.
通讯机构：
[Chen, Z ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
Image encryption;Hachimoji DNA coding;DNA computing;six-dimensional hyperchaotic system
摘要：
With the increasing awareness of privacy protection, people pay more and more attention to strengthening the security of image data transmitted over the network. Therefore, this paper designs a chaotic image encrypting algorithm based on dynamic Hachimoji DNA coding and computing to protect images. The Hachimoji DNA coding method provides richer coding rules to dynamically encode images than the traditional DNA coding method, improving the complexity and security of the encryption algorithm. First, the original image is rearranged and encoded with the dynamic Hachimoji DNA coding method according to the sorting and encoding controller sequence generated by a six-dimensional hyperchaotic system. Second, various DNA operations are performed on the encoded image. Among these operations, we not only use the common operations but also propose a new DNA operation called bitwise inversion. Finally, the DNA image is decoded using the dynamic decoding method to obtain the encrypted image. Experiments demonstrated that the image encryption algorithm has a good security effect and can effectively resist common attacks.
通讯机构：
[Zhong, C ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Hunan, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Hunan, Peoples R China.
关键词：
Image encryption;Chaotic map;Variable key space;Key association
摘要：
The security of images is of great importance given the current development of Internet technology. The existing encryption algorithms have some defects, such as the key space is not large enough and the encryption speed is slow. A fast image encryption algorithm with variable key space is proposed. The algorithm key space is dynamically changeable and the variable key space is associated with the initial condition of Hénon map, making this cryptosystem extremely sensitive to the key. The overall algorithm uses a permutation-diffusion-permutation-diffusion encryption structure. The first permutation process is implemented by cross-sampling and the first diffusion is implemented by modal operation. The second permutation is implemented using the chaotic sequence approach and the second diffusion is implemented using the XOR operation. The designed permutation and diffusion operations are executed with high efficiency, and the two different diffusion operations make the encryption process with nonlinear mapping capability, making the algorithm effective against existing typical differential attack schemes. Experiments show that the algorithm has a dynamically adjustable key space, high efficiency of algorithm encryption, good robustness, and effective resistance to statistical attack analysis and differential attack analysis.
期刊：
CURRENT BIOINFORMATICS,2024年19(10):919-932 ISSN：1574-8936
作者机构：
[Xianyou Zhu] College of Computer Science and Technology, Hengyang Normal University, Hengyang 421010, China;[Xiaoyu Yang; Lei Wang] Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha 410022, China.;[Liu Fan] College of Computer Science and Technology, Hengyang Normal University, Hengyang 421010, China<&wdkj&>Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha 410022, China.
摘要：
<jats:sec>
<jats:title>Introduction:</jats:title>
<jats:p>Microbes are intimately involved in the physiological and pathological
processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced
diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for
both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify
these relationships through traditional wet lab approaches.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods:</jats:title>
<jats:p>We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a
heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute
features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated
the probability of associations between microbes and drugs.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results:</jats:title>
<jats:p>Extensive experimental results showed that STNMDA surpassed existing state-of-the-art
models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of
STNMDA in confirming associations between microbes and drugs was demonstrated through case
validations.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusion:</jats:title>
<jats:p>Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.</jats:p>
</jats:sec>
通讯机构：
[Yu, XZ ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
Deep learning;rice diseases and pests;image recognition;object detection
摘要：
In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few -shot learning methods according to the network structure differences; and compares the performances of existing studies. Finally, the current issues and challenges are explored from the perspective of data acquisition, data processing, and application, providing possible solutions and suggestions. This study aims to review various DL models and provide improved insight into DL techniques and their cutting -edge progress in the prevention and management of rice diseases and pests.
摘要：
In recent years, the combination of deep learning and side-channel analysis has received extensive attention. Previous research has shown that the key recovery problem can be transformed into a classification problem. The performance of these models strongly depends on the size of the dataset and the number of instances in each target class. The training time is very long. In this paper, the key recovery problem is transformed into a similarity measurement problem in Siamese neural networks. We use simulated power traces and true power traces to form power pairs to augment data and simplify key recovery steps. The trace pairs are selected based on labels and added to the training to improve model performance. The model adopts a Siamese, CNN-based architecture, and it can evaluate the similarity between the inputs. The correct key is revealed by the similarity of different trace pairs. In experiments, three datasets are used to evaluate our method. The results show that the proposed method can be successfully trained with 1000 power traces and has excellent attack efficiency and training speed.
通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
Side-channel analysis;Semi-supervised learning;Hamming weight;Pseudo- label filter;Normal distribution
摘要：
Side -channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi -supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi -supervised learning generates many pseudo -labels. The performance of the SCA model can be reduced by some of these pseudo -labels. To solve this issue, we propose the HWFilter method to improve semisupervised SCA. This method uses a Hamming Weight Pseudo -label Filter (HWPF) to filter the pseudo -labels generated by the semi -supervised SCA model, which enhances the model's performance. Furthermore, we introduce a normal distribution method for constructing the HWPF. In the normal distribution method, the Hamming weights (HWs) of power traces can be obtained from the normal distribution of power points. These HWs are filtered and combined into a HWPF. The HWFilter was tested using the ASCADv1 database and the AES_HD dataset. The experimental results demonstrate that the HWFilter method can significantly enhance the performance of semi -supervised SCA models. In the ASCADv1 database, the model with HWFilter requires only 33 power traces to recover the key. In the AES_HD dataset, the model with HWFilter outperforms the current best semi -supervised SCA model by 12%.
通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词：
generalized Feistel structure;lightweight block cipher;low power;Permutation;S-box;smart door lock
摘要：
Smart door locks pose a large number of threats such as network attacks. Its storage area and power of cipher are severely limited because the wireless nodes of smart door locks are mostly battery-powered. Therefore, effective security solutions are urgently needed. In this paper, a new lightweight block cipher with low power named LPHD is proposed to ensure the security of the master control chip of the smart door lock terminal. We design a scheme of low power S-box and construct the two-stage permutation layer (TP structure) suitable for LPHD by filtering the sets of 8-bit permutations. LPHD proposes a variant of the 8-branch generalized Feistel structure (GFS) to realize that the bits of all branches are affected in one encryption round. The problem of slow diffusion in the standard Feistel structure is solved. The key schedule adopts the nonlinear design and reuses the encryption process of LPHD. It improves the security of the cipher and reduces hardware overhead. Moreover, we evaluate the hardware implementation and security of LPHD. The results show that LPHD for the unified encryption and decryption circuits requires only 1276 Gate Equivalents (GEs) and 1.914 mu$$ \upmu $$W on UMC 0.18 mu$$ \upmu $$m, which is better than other lightweight block ciphers such as SKINNY, PRESENT, and IVLBC. In summary, LPHD provides sufficient security for the master control chip of the smart door lock terminal. In this paper, a low power lightweight block cipher named LPHD is proposed, and the unified encryption and decryption circuits require only 1276 GEs and 1.914 mu$$ \upmu $$ W on UMC 0.18 mu$$ \upmu $$ m. We built a PCB of smart door lock and successfully used LPHD cipher to realize simulation encryption. image
摘要：
Low-energy lightweight block ciphers are essential for applications with extremely resource-constrained to reduce energy and maintain security. The trade-off between diffusion property and area is a widely studied issue in the design of low-energy block ciphers. In this paper, a low-energy lightweight block cipher named as GFLE is presented. The core cipher of GFLE uses a variant of the Generalized Feistel Structure (GFS) with 4-branch, which combines the Type-II GFS with the simplified Lai-Massey. The DRmax of GFLE has a one-round improvement over the Type-II GFS optimized by Suzaki et al and the security margin is achieved in a shorter number of rounds. Moreover, an S-box with low-energy and good cryptographic properties is proposed by searching combinations based on gate-level circuits using a depth-first strategy. It exhibits better security properties and hardware performance compared to other S-boxes. The block cipher GFLE is implemented in ASIC with UMC 0.18 mu m. It has been proved that the energy of GFLE is lower than Midori, WARP, SKINNY, CRAFT, etc in unified encryption and decryption (ED) circuits. GFLE reduces energy by 61.59% compared with SKINNY. The results show that GFLE in ED circuits consumes only 1596 Gate Equivalents (GEs) and 6.36 mu J/bit in area and energy, respectively.
摘要：
We propose ETBHD‐HMF, a novel hierarchical multimodal fusion network that comprehensively learns to align and fuse text instructions into complex image latent code space, achieving high‐quality and accurate hair design. Here, we offer an illustrative example of joint hair colour and hairstyle text as conditional inputs. Abstract Text‐based hair design (TBHD) represents an innovative approach that utilizes text instructions for crafting hairstyle and colour, renowned for its flexibility and scalability. However, enhancing TBHD algorithms to improve generation quality and editing accuracy remains a current research difficulty. One important reason is that existing models fall short in alignment and fusion designs. Therefore, we propose a new layered multimodal fusion network called ETBHD‐HMF, which decouples the input image and hair text information into layered hair colour and hairstyle representations. Within this network, the channel enhancement separation (CES) module is proposed to enhance important signals and suppress noise for text representation obtained from CLIP, thus improving generation quality. Based on this, we develop the weighted mapping fusion (WMF) sub‐networks for hair colour and hairstyle. This sub‐network applies the mapper operations to input image and text representations, acquiring joint information. The WMF then selectively merges image representation and joint information from various style layers using weighted operations, ultimately achieving fine‐grained hairstyle designs. Additionally, to enhance editing accuracy and quality, we design a modality alignment loss to refine and optimize the information transmission and integration of the network. The experimental results of applying the network to the CelebA‐HQ dataset demonstrate that our proposed model exhibits superior overall performance in terms of generation quality, visual realism, and editing accuracy. ETBHD‐HMF (27.8 PSNR, 0.864 IDS) outperformed HairCLIP (26.9 PSNR, 0.828 IDS), with a 3% higher PSNR and a 4% higher IDS.
摘要：
The system is based on the improvement and optimization of the existing workflow of the cinema, which has been found to have a large number of mechanical and time-consuming tasks. In line with the current trend of "electronic information", the system firstly introduces text recognition technology, which enables the rapid conversion of paper and electronic information. Secondly, the data visualization technology helps the cinema to have a better understanding of the recent sales situation. Compared to the previous system, this system avoids the boring and tedious mechanical manual entry work and is more in line with modern information management, which can effectively increase the efficiency and promote the long-term development of the cinema.
通讯机构：
[Chen, Z ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
color image encryption;hyperchaotic system;region of interest;security analysis
摘要：
The significance of safeguarding the security of image information has escalated significantly, owing to the exponential proliferation of digital images containing sensitive information being disseminated on the Internet. In this paper, we first propose a novel 4D hyperchaotic system and design a new image encryption algorithm in conjunction with the hyperchaotic system. The algorithm uses a split random swap permutation method to permute the image and combines the S-box to diffuse the image. To improve the diffusivity of this encryption algorithm, a cross-random diffusion method is designed to diffuse the image again. Then, we propose a region of interest (ROI) encryption scheme for images. This scheme can automatically identify irregular privacy targets in images and encrypt them. To ensure the security of the region of interest location information during transmission, the scheme compresses the location information of the privacy target using a run-length encoding technique and then embeds the compressed data into the ciphertext image using reversible steganography based on histogram shift. The experimental results and security analysis unequivocally demonstrate that the image encryption algorithm proposed in this paper exhibits robust resistance against a wide array of attacks, thereby ensuring a high level of security. Additionally, the devised image ROI encryption scheme effectively safeguards diverse privacy targets.
通讯机构：
[Chen, Z ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
fractional order;hyperchaotic system;image encryption;knight tour algorithm
摘要：
The security guarantee of data transmission is becoming more crucial as the frequency of information interchange rises. Ensuring the security of images is essential since they serve as a vital transmission medium. This research suggests an image encryption method that combines the knight tour algorithm with a 6D fractional order hyperchaotic system. First, chaotic sequences are produced using a fractional order hyperchaotic system, which is then utilized to index order and jumble the entire image. To retrieve the image after the second scrambling, choose the knight tour beginning point and run ten rounds of knight tour algorithms on the scrambled image. Thirdly, to maximize the efficiency of picture encryption, employ diffusion methods. The outcomes of the imaging experiment were lastly tested and assessed. The security of the image can be successfully guaranteed by a high-dimensional fractional order hyperchaotic system. This is because its high dimensionality gives it a larger key space than the low dimensional system. This is why it can resist attacks more effectively. After a series of evaluation experiments, it is obvious that this encryption scheme has good encryption performance.
通讯机构：
[Zhao, HH ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Hunan, Peoples R China.
关键词：
Talking face generation;Lip sync;Face motion;Virtual reality;Multimodality
摘要：
In this paper, we present TellMeTalk, an innovative approach for generating expressive talking face videos based on multimodal inputs. Our approach demonstrates robustness across various identities, languages, expressions, and head movements. It overcomes four key limitations of existing talking face video generation methods: (1) reliance on single -modal learning from audio or text, lacking the complementary nature of multimodal inputs; (2) deployment of traditional convolutional neural network generation, leading to restricted capture of spatial features; (3) the absence of natural head movements and expressions; and (4) limitations of artifacts, prominent boundaries caused by image overlapping, and unclear mouth regions. To address these challenges, we propose a face motion network to imbue character images with facial expressions and head movements. We also take text and reference audio as input to generate personalized audio. Furthermore, we introduce a generator equipped with a crossattention module and Fast Fourier Convolutional blocks to model spatial dependencies. Finally, a face restoration module is designed to reduce artifacts and prominent boundaries. Extensive experiments demonstrate our method produces high -quality expressive talking face videos. Compared to state-of-the-art approaches, our method exhibits superior performance in terms of video quality and precise synchronization of lip movements. The source code is available at https://github.com/lifemo/TellMeTalk.
作者机构：
[Xiang, Jiahao] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.;[Li, Lang] Hengyang Normal Univ, Hengyang 421002, Peoples R China.
通讯机构：
[Li, L ] H;Hengyang Normal Univ, Hengyang 421002, Peoples R China.
关键词：
Internet of Things;Lightweight block cipher;Field Programmable Gate Arrays(FPGA);Low-area;High-throughput
摘要：
The rapid growth of the Internet of Things (IoT) highlights the importance of lightweight cryptography in maintaining security. However, enhancing performance while ensuring the same level of security remains a significant challenge. This paper presents two innovative architectures for the CRAFT lightweight block cipher, aiming to enhance performance without compromising security. The novel Serial and Unrolled architectures are introduced to achieve low area usage and high throughput, respectively. Specifically, the Serial architecture reduces the datapath from 64 -bit to 4 -bit, significantly decreasing the area. The Unrolled architecture, on the other hand, minimizes latency from 32 to 16. Additionally, Boolean satisfiability (SAT) solvers are employed to identify a lower-cost area implementation of the S-Box. The proposed designs underwent evaluation on three distinct FPGA platforms: Artix-7, Kintex-7, and Spartan7. The results show that the low area design reduces area usage by 10.16% compared to the previous design. Additionally, the S-Box implementation achieves a significant area reduction of 28.9%. On the other hand, the unrolled design enhances the maximum throughput by 40.53% compared to the previous design. Therefore, the proposed designs could offer enhanced performance while maintaining security for IoT devices.
通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
Side-channel analysis;correlation power analysis;genetic algorithm;crossover;mutation
摘要：
Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual. Secondly, the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes. Finally, an intelligent search method is proposed to identify optimal parameters, further improving attack efficiency. Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform. In the case of simulation, SFGA-CPA reduces the number of traces by 27.3% and 60% compared to CPA based on multiple screening methods (MS-CPA) and CPA based on simple GA method (SGA-CPA) when the success rate reaches 90%. Moreover, real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.
摘要：
In traditional stroke-based image-to-painting techniques, strokes are often repeated in already drawn areas, resulting in ineffective rendering, low utilization of strokes, and low image quality. This paper presents an approach for image-to-painting that dynamically assigns strokes based on the intrinsic characteristics of different regions in the target image. The proposed method uses an estimation technique to determine the amount of information in different image regions to generate a sequence of content richness. The number of strokes is then dynamically assigned based on the sequence of content richness, which results in a sequence of strokes. Afterward, the distribution of strokes on the canvas is flexibly adjusted based on their sequence, allowing for the accurate rendering of the target image. In addition, the original network model is optimized and improved to make the stroke rendering more accurate and thus obtain a final result that better fits the input image. The experimental results show that the proposed approach can scientifically allocate strokes based on the inherent differences in different image regions, which leads to effective rendering, improved utilization of strokes, and higher visual performance. Furthermore, many indicators, such as the peak signal-to-noise ratio, structural similarity, and Learned Perceptual Image Patch Similarity demonstrate higher image quality compared with those obtained by traditional technologies. (c) 2024 SPIE and IS&T
摘要：
Medical image encryption is essential to protect the privacy and confidentiality of patients' medical records. Deep learning-based encryption, which leverages the nonlinear characteristics of neural networks, has emerged as a promising new method for protecting medical images. In this paper, we present insights into deep learning-based medical image encryption and propose a novel end-to-end medical image encryption scheme based on these insights that leverages feature encoding and decoding for encrypting and decrypting images. Firstly, we explore a method that combines keys generated by the Logistic Map with encoded plaintext image features to improve network diffusion performance. Secondly, we employ a reversible neural network to enhance plaintext image reconstruction while maintaining encryption effectiveness. Finally, we propose a series of novel loss functions to measure the cost with the ideal cryptographic algorithm and continuously optimize our network. Experimental results demonstrate that our scheme improves the performance of image encryption and decryption and resists brute force attacks, statistical attacks, noise and cropping attacks.
