Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational h...Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.展开更多
The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor cor...The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.展开更多
Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the u...Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.展开更多
Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed...Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed speech.However,the performance of these GAN-based methods is worse than those of masking-based methods.To tackle this problem,we propose speech enhancement method with a residual dense generative adversarial network(RDGAN)contributing to map the log-power spectrum(LPS)of degraded speech to the clean one.In detail,a residual dense block(RDB)architecture is designed to better estimate the LPS of clean speech,which can extract rich local features of LPS through densely connected convolution layers.Meanwhile,sequential RDB connections are incorporated on various scales of LPS.It significantly increases the feature learning flexibility and robustness in the time-frequency domain.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes.It indicates that our method is more generalized in untrained conditions.展开更多
In block ciphers,the nonlinear components,also known as sub-stitution boxes(S-boxes),are used with the purpose of inducing confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the ...In block ciphers,the nonlinear components,also known as sub-stitution boxes(S-boxes),are used with the purpose of inducing confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers(GI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.But the prime field dependent on the Elliptic curve(EC)provides one S-box at a time by fixing three parameters a,b,and p.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.展开更多
The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability ...The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability and its usual properties improving and stabilization techniques used. Thus, fonio straws and shea butter residues are incorporated into tow soil matrix. The objective of this study is to develop a construction eco-material by recycling agricultural and biopolymer by-products in compressed earth blocks (CEB) stabilization and analyze these by-products’ influence on CEB usual properties. To do this, compressed stabilized earth blocks (CSEB) composed of clay and varying proportion (3% to 10%) of fonio straw and shea butter residue incorporated were subjected to thermophysical, flexural, compressive, and durability tests. The results obtained show that the addition of fonio straw and shea butter residues as stabilizers improves compressed stabilized earth blocks thermophysical and mechanical performance and durability. Two different clay materials were studied. Indeed, for these CEB incorporating 3% fonio straw and 3% - 10% shea butter residue, the average compressive strength and three-point bending strength values after 28 days old are respectively 3.478 MPa and 1.062 MPa. In terms of CSEB thermal properties, the average thermal conductivity is 0.549 W/m·K with 3% fonio straw and from 0.667 to 0.798 W/m. K is with 3% - 10% shea butter residue and the average thermal diffusivity is 1.665.10-7 m2/s with 3% FF and 2.24.10-7 m2/s with 3.055.10-7 m2/s with 3% - 10% shea butter residue, while the average specific heat mass is between 1.508 and 1.584 kJ/kg·K. In addition, the shea butter residue incorporated at 3% - 10% improves CSEB water repellency, with capillary coefficient values between 31 and 68 [g/m2·s]1/2 and a contact angle between 43.63°C and 86.4°C. Analysis of the results shows that, it is possible to use these CSEB for single-storey housing construction.展开更多
Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree ...Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree of muscle relaxation according to clinical signs when after using rocuronium or cis-atracurium in general anesthesia.Methods: 500 adults were implemented with propofol-remifentanil intravenous anesthesia or sevoflurane inhalation anesthesia. Rocuronium and cis-atracurium were given, respectively. The TOFr was observed with blind method by TOF Watch SX monitor during anesthesia.Results: The mean TOFr=0.53±0.38 at the end of operation,including 275 cases of 0<TOFr<0.9 and 112 cases of TOFr=0. The mean TOFr=0.97±0.12 at extubation, including 60 cases of TOFr<0.9. The incidence of residual neuromuscular blockade at extubation showed an increasing trend with the increase of age or body mass index. The average TOFr value at extubation, which interval time over 10 min after neostigmine administration to extubation was significant higher than that of interval time less than 10 min.Conclusions:There has 12% patients with TOFr<0.9 when extubation by estimating rocuronium and cis-atracurium effect with clinical signs and experience, it has a hidden danger of residual neuromuscular blockade. The main risk factors to increasing the incidence of residual neuromuscular blockade are growing old and the short time of administrating muscle relaxants or neostigmine to extubation.展开更多
Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and ...Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and Eucalyptus grandis) and an agricultural waste—rice husk (Oriza sativa) were combined with Portland cement type V (high initial strength), modified by polymer styrene-butadiene (SBR) addition. Hollow blocks produced with Eucalyptus grandis and rice husk residues showed better compressive strength;however, those produced with residues derived from Pinus caribaea presented non-satisfactory results, due to the particle size that was used.展开更多
This paper proposes a two-parameter block triangular splitting(TPTS)preconditioner for the general block two-by-two linear systems.The eigenvalues of the corresponding preconditioned matrix are proved to cluster aroun...This paper proposes a two-parameter block triangular splitting(TPTS)preconditioner for the general block two-by-two linear systems.The eigenvalues of the corresponding preconditioned matrix are proved to cluster around 0 or 1 under mild conditions.The limited numerical results show that the TPTS preconditioner is more efficient than the classic block-diagonal and block-triangular preconditioners when applied to the flexible generalized minimal residual(FGMRES)method.展开更多
In block ciphers,the nonlinear components,also known as substitution boxes(S-boxes),are used with the purpose to induce confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the poi...In block ciphers,the nonlinear components,also known as substitution boxes(S-boxes),are used with the purpose to induce confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves,chaotic maps,and Gaussian integers has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers(EI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.However,in the same way,by taking three fixed parameters only one S-box is obtained through a prime field-dependent Elliptic curve(EC),chaotic maps,and Gaussian integers.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.展开更多
SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a v...SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a variety of ways,but it is still difficult to achieve a precisely located fault attacks at a low cost,whereas a Hardware Trojan(HT)can realize this.Temperature,as a physical quantity incidental to the operation of a cryptographic device,is easily overlooked.In this paper,a temperature-triggered HT(THT)is designed,which,when activated,causes a specific bit of the intermediate state of the SKINNY-64-64 to be flipped.Further,in this paper,a THT-based algebraic fault analysis(THT-AFA)method is proposed.To demonstrate the effectiveness of the method,experiments on algebraic fault analysis(AFA)and THT-AFA have been carried out on SKINNY-64-64.In the THT-AFA for SKINNY-64-64,it is only required to activate the THT 3 times to obtain the master key with a 100%success rate,and the average time for the attack is 64.57 s.However,when performing AFA on this cipher,we provide a relation-ship between the number of different faults and the residual entropy of the key.In comparison,our proposed THT-AFA method has better performance in terms of attack efficiency.To the best of our knowledge,this is the first HT attack on SKINNY-64-64.展开更多
基金National Natural Science Foundation of China(Grant No.62073227)Liaoning Provincial Science and Technology Department Foundation(Grant No.2023JH2/101300212).
文摘Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.
基金This was supported partially by Sichuan Science and Technology Program under Grants 2019YJ0356,21ZDYF2484,21GJHZ0061Scientific Research Foundation of Education Department of Sichuan Province under Grant 18ZB0117.
文摘The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.
基金supported by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002the National Nature Science Foundation of China(NSFC)under Grant No.61571106.
文摘Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.
基金This work is supported by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002the National Nature Science Foundation of China(NSFC)under Grant No.61571106。
文摘Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed speech.However,the performance of these GAN-based methods is worse than those of masking-based methods.To tackle this problem,we propose speech enhancement method with a residual dense generative adversarial network(RDGAN)contributing to map the log-power spectrum(LPS)of degraded speech to the clean one.In detail,a residual dense block(RDB)architecture is designed to better estimate the LPS of clean speech,which can extract rich local features of LPS through densely connected convolution layers.Meanwhile,sequential RDB connections are incorporated on various scales of LPS.It significantly increases the feature learning flexibility and robustness in the time-frequency domain.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes.It indicates that our method is more generalized in untrained conditions.
文摘In block ciphers,the nonlinear components,also known as sub-stitution boxes(S-boxes),are used with the purpose of inducing confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers(GI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.But the prime field dependent on the Elliptic curve(EC)provides one S-box at a time by fixing three parameters a,b,and p.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.
文摘The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability and its usual properties improving and stabilization techniques used. Thus, fonio straws and shea butter residues are incorporated into tow soil matrix. The objective of this study is to develop a construction eco-material by recycling agricultural and biopolymer by-products in compressed earth blocks (CEB) stabilization and analyze these by-products’ influence on CEB usual properties. To do this, compressed stabilized earth blocks (CSEB) composed of clay and varying proportion (3% to 10%) of fonio straw and shea butter residue incorporated were subjected to thermophysical, flexural, compressive, and durability tests. The results obtained show that the addition of fonio straw and shea butter residues as stabilizers improves compressed stabilized earth blocks thermophysical and mechanical performance and durability. Two different clay materials were studied. Indeed, for these CEB incorporating 3% fonio straw and 3% - 10% shea butter residue, the average compressive strength and three-point bending strength values after 28 days old are respectively 3.478 MPa and 1.062 MPa. In terms of CSEB thermal properties, the average thermal conductivity is 0.549 W/m·K with 3% fonio straw and from 0.667 to 0.798 W/m. K is with 3% - 10% shea butter residue and the average thermal diffusivity is 1.665.10-7 m2/s with 3% FF and 2.24.10-7 m2/s with 3.055.10-7 m2/s with 3% - 10% shea butter residue, while the average specific heat mass is between 1.508 and 1.584 kJ/kg·K. In addition, the shea butter residue incorporated at 3% - 10% improves CSEB water repellency, with capillary coefficient values between 31 and 68 [g/m2·s]1/2 and a contact angle between 43.63°C and 86.4°C. Analysis of the results shows that, it is possible to use these CSEB for single-storey housing construction.
基金Guangdong science and technology plan project (2013B31800248).
文摘Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree of muscle relaxation according to clinical signs when after using rocuronium or cis-atracurium in general anesthesia.Methods: 500 adults were implemented with propofol-remifentanil intravenous anesthesia or sevoflurane inhalation anesthesia. Rocuronium and cis-atracurium were given, respectively. The TOFr was observed with blind method by TOF Watch SX monitor during anesthesia.Results: The mean TOFr=0.53±0.38 at the end of operation,including 275 cases of 0<TOFr<0.9 and 112 cases of TOFr=0. The mean TOFr=0.97±0.12 at extubation, including 60 cases of TOFr<0.9. The incidence of residual neuromuscular blockade at extubation showed an increasing trend with the increase of age or body mass index. The average TOFr value at extubation, which interval time over 10 min after neostigmine administration to extubation was significant higher than that of interval time less than 10 min.Conclusions:There has 12% patients with TOFr<0.9 when extubation by estimating rocuronium and cis-atracurium effect with clinical signs and experience, it has a hidden danger of residual neuromuscular blockade. The main risk factors to increasing the incidence of residual neuromuscular blockade are growing old and the short time of administrating muscle relaxants or neostigmine to extubation.
文摘Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and Eucalyptus grandis) and an agricultural waste—rice husk (Oriza sativa) were combined with Portland cement type V (high initial strength), modified by polymer styrene-butadiene (SBR) addition. Hollow blocks produced with Eucalyptus grandis and rice husk residues showed better compressive strength;however, those produced with residues derived from Pinus caribaea presented non-satisfactory results, due to the particle size that was used.
基金the National Natural Science Foundation of China under Grant Nos.61273311 and 61803247.
文摘This paper proposes a two-parameter block triangular splitting(TPTS)preconditioner for the general block two-by-two linear systems.The eigenvalues of the corresponding preconditioned matrix are proved to cluster around 0 or 1 under mild conditions.The limited numerical results show that the TPTS preconditioner is more efficient than the classic block-diagonal and block-triangular preconditioners when applied to the flexible generalized minimal residual(FGMRES)method.
基金extend their appreciation to the Deanship of Scientific Research at King Khalid University,for funding this work through the General Research Groups Program under Grant No.R.G.P.2/109/43.
文摘In block ciphers,the nonlinear components,also known as substitution boxes(S-boxes),are used with the purpose to induce confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves,chaotic maps,and Gaussian integers has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers(EI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.However,in the same way,by taking three fixed parameters only one S-box is obtained through a prime field-dependent Elliptic curve(EC),chaotic maps,and Gaussian integers.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.
基金supported in part by the Natural Science Foundation of Heilongjiang Province of China(Grant No.LH2022F053)in part by the Scientific and technological development project of the central government guiding local(Grant No.SBZY2021E076)+2 种基金in part by the PostdoctoralResearch Fund Project of Heilongjiang Province of China(Grant No.LBH-Q21195)in part by the Fundamental Research Funds of Heilongjiang Provincial Universities of China(Grant No.145209146)in part by the National Natural Science Foundation of China(NSFC)(Grant No.61501275).
文摘SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a variety of ways,but it is still difficult to achieve a precisely located fault attacks at a low cost,whereas a Hardware Trojan(HT)can realize this.Temperature,as a physical quantity incidental to the operation of a cryptographic device,is easily overlooked.In this paper,a temperature-triggered HT(THT)is designed,which,when activated,causes a specific bit of the intermediate state of the SKINNY-64-64 to be flipped.Further,in this paper,a THT-based algebraic fault analysis(THT-AFA)method is proposed.To demonstrate the effectiveness of the method,experiments on algebraic fault analysis(AFA)and THT-AFA have been carried out on SKINNY-64-64.In the THT-AFA for SKINNY-64-64,it is only required to activate the THT 3 times to obtain the master key with a 100%success rate,and the average time for the attack is 64.57 s.However,when performing AFA on this cipher,we provide a relation-ship between the number of different faults and the residual entropy of the key.In comparison,our proposed THT-AFA method has better performance in terms of attack efficiency.To the best of our knowledge,this is the first HT attack on SKINNY-64-64.
