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.
期刊:
Journal of Electronic Imaging,2023年32(4) ISSN:1017-9909
通讯作者:
Tian, XM
作者机构:
[Guo, Yanyu; Xiao, Yanting; Tian, Xiaomei] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
通讯机构:
[Tian, XM ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词:
Object detection;Detection and tracking algorithms;Neck;Head;Convolution;Feature fusion;Feature extraction;Water;Evolutionary algorithms;Image processing
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
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 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.
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.
关键词:
Deformation;Windows;Object detection;Education and training;Image processing;Feature extraction;Cameras;Distortion;Head;Data modeling
摘要:
Retail product detection in fisheye camera capture scenes frequently suffers from excessive object occlusion and deformation, as well as difficulty in distinguishing products with small fine-grained differences, so accurately classifying and localizing products in these images presents a challenge for computer vision. We propose an efficient product detection network called EPformer by fusing a visual transformer and convolutional neural network to reliably detect retail products in fisheye images. We employ a shifted window strategy for feature information interaction across windows to more precisely detect products due to the issue of dense occlusion of products. To address the issue of excessive product deformation brought on by fisheye cameras, we develop a deformation image processing module without explicit correction and embed it into the path aggregation network structure. This enables the model to efficiently capture product geometric changes and conduct feature fusion. To address the issue of differentiating fine-grained products, we design an effective coordinate squeeze-excitation (ECSE) attention module that can capture the fine-grained texture and boundary information differences between individuals in terms of spatial and channel relationships. The inability to differentiate fine-grained products can be solved by training the ECSE module in tandem with the decoupled head. The experimental results demonstrate that EPformer is a potent product detection model with a 4.9% higher mean average precision than the state-of-the-art method (YOLOX) on the fisheye product image dataset. In addition, the EPformer model can effectively detect products in fisheye images on the Jeston Xavier NX embedded device to meet the application requirements in realistic scenarios.
通讯机构:
[Huihuang Zhao] 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
关键词:
VStyclone;Voice clone;Efficient tone extractor;Style synthesizer;Transformer;Vocoder
摘要:
Image transmission is happening more frequently in this era of technologically sophisticated digital information. Additionally, more individuals are becoming aware of its importance. In order to secure images, many academics are participating in research, which is advantageous for guaranteeing data security. In order to strengthen the security of images during transmission, we have investigated new encryption algorithms to guarantee this. First, a current representing the Lorenz chaotic system is introduced into the neuron model. The neuron model generates sequences after receiving the current signal. The next move is made as the current shifts depending on whether the resulting sequences are chaotic or not. If so, the subsequent operation is carried out; otherwise, the current is altered until chaotic sequences are produced. Second, a global scrambling with de-duplication technique is used to scramble the image using the resulting chaotic sequences. To complete the dislocation effect, the Latin square is used to dislocate the image after the initial dislocation. Fourth, the image that has been scrambled is subjected to two rounds of additive mode diffusion. They are diffusion in the forward additive mode and diffusion in the inverse additive mode. Lastly, to improve the diffusion effect, the image is diffused in the finite domain. Eventually, the encrypted image is obtained. After evaluation tests and comparison with related literature, it can be found that the algorithm of this study has certain advantages. Also, the resistance to attack is good. It can protect the security of the image.
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
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.
通讯机构:
[Lang Li] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
关键词:
internet of things;5G;dynamic S-box;bit-slice technology;lightweight block cipher
关键词:
Internet of Things (IoT);involutive;lightweight block cipher;permutation;S-box;security
摘要:
Nowadays, the use of the Internet of Things has reached a commanding height in a new round of economic and technological upsurge. Its data transmission security has attracted much attention. It is well known that substitution permutation networks (SPNs) ciphers with high diffusion are not advantageous in unified encryption and decryption circuits with extremely resources constrained. Although some research has been carried out to address this issue, there are still insufficiencies. In this article, we propose a new 64-bit lightweight block cipher based on SPN named IVLBC, whose key allows 80 and 128 bits. The components of IVLBC are involutions. In particular, we propose a Feistel with tree structure to obtain a compact and involutive S-box. Also, the nibble-based involutive permutation is proposed to obtain the involutive permutation. Decryption can reuse encrypted code and circuitry in both software and hardware implementations. We prove that the costs of IVLBC are less than PRESENT, PRINCE, Midori, I-PRESENTTM, CRAFT, etc., in unified encryption and decryption circuits. In addition, we conduct other performance tests on IVLBC such as the differential attack, linear attack, integral attack, algebraic attack, invariant attacks, etc.
摘要:
The number of industrial Internet of Things (IoT) users is increasing rapidly. Lightweight block ciphers have started to be used to protect the privacy of users. Hardware-oriented security design should fully consider the use of fewer hardware devices when the function is fully realized. Thus, this paper designs a lightweight block cipher IIoTBC for industrial IoT security. IIoTBC system structure is variable and flexibly adapts to nodes with different security requirements. This paper proposes a 4x4 S-box that achieves a good balance between area overhead and cryptographic properties. In addition, this paper proposes a preprocessing method for 4x4 S-box logic gate expressions, which makes it easier to obtain better area, running time, and power data in ASIC implementation. Applying it to 14 classic lightweight block cipher S-boxes, the results show that is feasible. A series of performance tests and security evaluations were performed on the IIoTBC. As shown by experiments and data comparisons, IIoTBC is compact and secure in industrial IoT sensor nodes. Finally, IIoTBC has been implemented on a temperature state acquisition platform to simulate encrypted transmission of temperature in an industrial environment.
摘要:
In this paper, a lossless image compression and encryption algorithm combining JPEG-LS, neural networks and hyperchaotic mapping is proposed to protect the privacy of digital images and reduce data storage space. Firstly, we design a new 2-Dimensional Logistic-Like Hyperchaotic Map (2DLLHM), which has more complex dynamics than some existing known chaotic systems, and can be used to build a good pseudorandom sequence generator. Secondly, to compress images efficiently, we design a new pixel predictor by combining the MED (Median Edge Detector) of JPEG-LS with MLP (Multilayer Perceptron). This predictor is called MMP. The MMP can effectively improve the prediction effect of edge texture area. On this basis, a threshold segmentation method is proposed. The method combined with MMP, run-length coding and Huffman coding can further improve the image compression ratio. Finally, to avoid some of the existing weak encryption designs, we construct a multi-round nonlinear diffusion structure with more excellent diffusion performance. Experiments show that the algorithm achieves a good compression ratio and can resist brute force attacks, statistical attacks, chosen-plaintext attacks and chosen-ciphertext attacks.
通讯机构:
[Li, QP ] 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.
摘要:
The graph G of order n is an L-borderenergetic graph which means it has the same Laplacian energy as the complete graph Kn. In this paper, we find that the combination of complete bi-partite graphs and stars can construct infinite numbers of infinite classes L-borderenergetic graphs. We give two infinite numbers of infinite classes L-borderenergetic graphs and two infinite classes L-borderenergetic graphs under the operators union, join and their mixed. This research could provide experience for further study the structural characteristics of L-borderenergetic graphs.
通讯机构:
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词:
area-optimized;high throughput;Internet of Things;lightweight;LILLIPUT block cipher
摘要:
The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. Summary The widespread use of Internet of Things devices has increased the demand for lower cost and more efficient lightweight ciphers. However, there is a difficult trade‐off between cost and efficiency for lightweight block ciphers. The optimizations of area and throughput are important for some constrained environments. This paper proposes two novel hardware architectures for the LILLIPUT cipher. In the novel low area structure, a new permutation layer is provided for LILLIPUT. The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The experimental results show that the number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. The total area is reduced by about 18%. For high throughput structure, this paper provides 2‐round, 5‐round, and 15‐round loop unrolling designs for LILLIPUT to improve throughput. The experimental results show that the throughput of the 5‐round loop unrolling structure reaches a good level, which is relatively the most cost‐effective. In practical application, ciphers can be unrolled implementations according to the needs of devices to improve the execution speed, which can greatly reduce the execution time and complexity of the algorithm.