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.
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.
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：
[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.
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：
[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.
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.
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.
摘要：
Camouflaged instance segmentation (CIS) focuses on handling instances that attempt to blend into the background. However, existing CIS methods emphasize global interactions but overlook hidden clues at various scales, resulting in inaccurate recognition of camouflaged instances. To address this, we propose a multi-scale pooling network (MSPNet) to mine the hidden cues offered by the camouflaged instances at various scales. The network achieves an enhanced fusion of multi-scale information mainly through multilayer pooling. Specifically, the pyramid pooling transformer (P2T) is utilized as a robust backbone for extracting multi-scale features. Then, we introduce an end-to-end pooling learning transformer (PLT) to obtain instance-aware parameters and high-quality mask features. To further augment the fusion of various mask features, we design a novel multi-scale complementary feature pooling (MCFP) module. Additionally, we also suggest an instance normalization module with fused spatial attention (FSA-IN) to combine instance-aware parameters and mask features, resulting in the final camouflaged instances. Experimental results show the effectiveness of MSPNet, surpassing existing CIS models on the COD10K-Test and NC4K datasets, with respective average precision (AP) scores of 49.6% and 53.4%. This demonstrates the effectiveness of the proposed approach in detecting camouflaged instances. Our code will be published at
https://github.com/another-u/MSPNet-main
.
通讯机构：
[Zhao, HH ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421008, Peoples R China.
关键词：
Convex optimization;block compressive sensing;split Bregman iteration;Poisson function
摘要：
To improve reconstruction performance in imagery compressive sensing, the present paper changes solving a block image compressive sensing reconstruction into a convex optimization problem. First, a Total-Variation norm minimization constraints model that includes both L1 and L2 norm functions is established. The split Bregman iterative method solves the model with convex optimization. Then, a robust adaptive image block compressive sensing algorithm is studied based on an analysis of the image features. The image is divided into blocks, and an overlap image block compressive reconstruction method is proposed. Finally, to solve the block effect caused by block compressive sensing reconstruction, a novel image overlap block compressive sensing reconstruction based on the Poisson function is suggested to avoid the block effect in the reconstruction process. The experimental results show that compared with other traditional compressive sensing reconstruction algorithms, the proposed method can generate a better image reconstruction result. According to the PSNR evaluation, when the sampling rate is 0.3, the proposed method is improved by more than 20.98% compared to the conventional techniques, and according to the SSIM evaluation, it has improved by more than 11.92% from the traditional methods. We can also find that the proposed method has better construction effect for traffic sign image recognition compared with ordinary natural image reconstruction. When the sampling rate is only 0.1, the PSNR value reaches 44.28dB, and the SSIM reconstruction accuracy reaches 98.14%. After reconstructing different types and characteristic images, it is supported that the proposed algorithm has good robustness and anti-noise performance.
通讯机构：
[Zhao, HH ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词：
Tracking-by-detection;Multi-object tracking;Occlusion;Global information
摘要：
As a fundamental task in computer vision, multi -object tracking (MOT) has gained increasing attention due to its commercial and academic potential. However, accurately tracking multiple objects is highly challenging. The problems of object occlusion, deformation, and real-time requirements have long been obstacles to be tackled in the field of multi -object tracking. In this paper, we first propose a novel approach to address the problem of tracklet fragmentation caused by occlusion and deformation by establishing a global linking model to obtain global information on trajectories. Then, we reduce camera jitter using camera motion compensation to more accurately locate objects in the moving scene. Next, to address the issue of linear interpolation that ignores a large amount of motion information, we utilize Gaussian process regression for smooth interpolation to fill in missing detections and reduce the noise covariance of the Kalman filter through adaptive computation. We integrate our work together and ultimately propose a simple, real-time, and robust multi -object tracker named TPTrack. Through extensive experiments, our proposed TPTrack achieves HOTA scores of 63.5, 61.7, and 56.8 on the MOT17, MOT20, and DanceTrack datasets, respectively. Notably, it exhibits a significant 19% enhancement over the state-of-the-art approach, specifically on the DanceTrack dataset. Furthermore, TPTrack operates at a speed of 33.7 FPS on a single GPU. The source code is available at https://github.com/godHhh/TPTrack.
摘要：
A well-defined cost function is a key issue for image steganography to minimize the embedding distortion. In recent years, deep learning has been introduced into image steganography to automatically learn embedding costs and improve steganographic security. For most existing generative adversarial network (GAN) based cost learning works, the generator usually adopts an encoder-decoder architecture. However, due to repeated encoding and decoding operations, this architecture is prone to information loss, making the generator difficult to well capture fine-grained image features. In this work, we propose a novel GAN-based image steganography work that improves the cost function by learning better embedding probability maps. Specifically, we design an attention mechanism to be integrated into the U -Net architecture, which enables the generator to concentrate on texture -rich regions of input images. Moreover, an extra input stream, namely the enhanced image, is introduced into the generator, improving the generator's ability to learn structural features from input images. Different skip connections are used for different input streams to facilitate information flow between different layers. Extensive experimental results demonstrate that the proposed approach effectively learns the embedding probability maps and achieves superior security against various steganalysis attacks.
摘要：
Recent research on text -guided image style transfer using CLIP (Contrastive Language -Image Pre -training) models has made good progress. Existing work does not rely on additional generative models, but it cannot guarantee the quality of the generated images, and often suffers from problems such as distortion of content images and uneven stylization of the generated images. To address such problems, this work proposes the TextStyler model, a CLIP -based approach for text -guided style transfer. In the TextStyler model, we propose a style transformation network STNet, which consists of an encoder and a multi -scale decoder. The network can capture the hierarchical features of the content image, and the decoder feature fusion module in the network, designed based on the channel attention mechanism, helps the network to maximize the retention of the detailed information of the content image while realizing texture transfer. In addition, we design a patch -wise perceptual loss, which is able to transfer the stylized texture to each local region of the image and improve the balance of model stylization. The experimental results show that the TextStyler model can achieve a wider range of style transfer than existing methods using stylized images, and the generated artistic images are more in line with human visual perception than state-of-the-art text -guided style transfer methods.
作者：
Kuang, Juanli*;Cao, Xiawei;Li, Songxiao;Li, Lang
期刊：
Journal of King Saud University - Computer and Information Sciences,2024年36(1) ISSN：1319-1578
通讯作者：
Kuang, Juanli;Li, L
作者机构：
[Kuang, Juanli] Macau Univ Sci & Technol, Fac Innovat Engn, Macau 999078, Peoples R China.;[Kuang, Juanli; Li, Lang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Cao, Xiawei; Kuang, Juanli; Li, Lang] Applicat Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc, Hengyang 421002, Peoples R China.;[Li, Songxiao] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China.
通讯机构：
[Kuang, JL; Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词：
Internet of Things;Block cipher;Lightweight cryptography;GFN;Pseudo -random;Negative feedback mechanism
摘要：
The Internet of Things (IoT) has gained popularity in various fields, including components such as embedded devices and wireless sensors. However, ensuring the security of data transmission from these devices is of critical importance. In light of these challenges, a novel lightweight encryption method called DRcipher is proposed in this paper for resource-constrained IoT devices. DRcipher has a 64-bit block size and supports either 96-bit or 128-bit key sizes. In order to improve the security, DRcipher employs a pseudo-random number of encryption rounds determined by the primary key. DRcipher adopts the structure of Generalized Feistel Network (GFN) with 4 branches, and its round functions consist of F-function, FF-function and RP permutation components. In particular, there is a negative feedback mechanism between the FF-function and the overall round of encryption functions. In addition, DRcipher is synthesised using Synopsys Design Compiler version A-2007.12-SP1 and the UMCL18G212T3 standard cell library. DRcipher-96 has an area footprint of 1546 Gate Equivalents (GE), while DRcipher-128 has a slightly larger area footprint of 1646 GE. Moreover, a comprehensive security analysis shows that the proposed DRcipher ensures high-level security redundancy against differential cryptanalysis, linear cryptanalysis, and so on.
摘要：
Unmanned missions have become more and more popular in recent years. The related technologies of unmanned ground vehicles and unmanned aerial vehicles are growing rapidly, but research on unmanned surface vehicles (USVs) is rare. Water surface object detection algorithms play a crucial role in the field of USVs. However, achieving an object detection algorithm that balances speed and accuracy in the presence of interference is a difficult challenge. We proposed a network, DBCR-YOLO, that improved the detection accuracy while meeting real-time requirements. Based on YOLOv5, we added an additional detection head for detecting tiny objects. Then, we replaced the downsampling in YOLOv5's backbone network with the proposed double sampling mechanism to solve the problem that paying attention to the key features of objects cannot be done in the downsampling process of YOLOv5. Finally, we substituted the proposed BCR neck for YOLOv5's neck, thus improving the fusion of features between different scales based on fewer parameters and fewer calculations. We tested our network on the water surface object detection dataset. Compared with YOLOv5, DBCR-YOLO improved the detection accuracy by 3.4%. At the same time, DBCR-YOLO achieved the highest accuracy in comparison with other networks. (c) 2023 SPIE and IS&T
摘要：
The ARX-based lightweight block cipher is widely used in resource-constrained IoT devices due to fast , simple operation of software and hardware platforms. However, there are three weaknesses to ARX-based lightweight block ciphers. Firstly, only half of the data can be changed in one round. Secondly, traditional ARX-based lightweight block ciphers are static structures, which provide limited security. Thirdly, it has poor diffusion when the initial plaintext and key are all 0 or all 1. This paper proposes a new dynamic ARX-based lightweight block cipher to overcome these weaknesses, called DABC. DABC can change all data in one round, which overcomes the first weakness. This paper combines the key and the generalized two-dimensional cat map to construct a dynamic permutation layer P1, which improves the uncertainty between different rounds of DABC. The non-linear component of the round function alternately uses NAND gate and , gate to increase the complexity of the attack, which overcomes the third weakness. Meanwhile, this paper proposes the round-based architecture of DABC and conducted ASIC and FPGA implementation. The hardware results show that DABC has less hardware resource and high throughput. Finally, the safety evaluation results show that DABC has a good avalanche effect and security.
通讯机构：
[Mugang Lin] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.
作者机构：
[Zhu, Xianyou; Fan, Liu] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421010, Peoples R China.;[Wang, Lei; Fan, Liu] Changsha Univ, Inst Bioinformat Complex Network Big Data, Changsha 410022, Peoples R China.;[Wang, Lei] Changsha Univ, Big Data Innovat & Entrepreneurship Educ Ctr Huna, Changsha 410022, Peoples R China.;[Zhu, Xianyou] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421010, Peoples R China.;[Wang, Lei] Changsha Univ, Inst Bioinformat Complex Network Big Data, Changsha 410022, Peoples R China.
通讯机构：
[Zhu, XY ; Wang, L ] ;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421010, Peoples R China.;Changsha Univ, Inst Bioinformat Complex Network Big Data, Changsha 410022, Peoples R China.;Changsha Univ, Big Data Innovat & Entrepreneurship Educ Ctr Huna, Changsha 410022, Peoples R China.
摘要：
Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.
