We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for the channel state information of both the eavesdropping and interference channels. Based on the asynchronous distributed pricing model, the proposed scheme maximizes the total utility of all the users, where each user’s utility function is defined as the secrecy rate minus the interference cost imposed on other users. Using the weighted minimum mean square error, Schur complement and sign-definiteness techniques, the original non-concave optimization problem is approximated with high accuracy as a quasi-concave problem, which can be solved by the alternating convex search method. Simulation results consolidate our theoretical analysis and show that the proposed scheme outperforms the artificial noise-assisted interference alignment and minimum total mean-square error-based schemes.
{"title":"Distributed Robust Artificial-Noise-Aided Secure Precoding for Wiretap MIMO Interference Channels","authors":"Zhengmin Kong;Jing Song;Shaoshi Yang;Li Gan;Weizhi Meng;Tao Huang;Sheng Chen","doi":"10.1109/TIFS.2024.3486548","DOIUrl":"10.1109/TIFS.2024.3486548","url":null,"abstract":"We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for the channel state information of both the eavesdropping and interference channels. Based on the asynchronous distributed pricing model, the proposed scheme maximizes the total utility of all the users, where each user’s utility function is defined as the secrecy rate minus the interference cost imposed on other users. Using the weighted minimum mean square error, Schur complement and sign-definiteness techniques, the original non-concave optimization problem is approximated with high accuracy as a quasi-concave problem, which can be solved by the alternating convex search method. Simulation results consolidate our theoretical analysis and show that the proposed scheme outperforms the artificial noise-assisted interference alignment and minimum total mean-square error-based schemes.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10130-10140"},"PeriodicalIF":6.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/TIFS.2024.3486611
Lvjun Chen;Di Xiao;Xiangli Xiao;Yushu Zhang
Federated learning (FL) facilitates collaborative training of a global model without sharing the participants’ raw data. Nevertheless, existing FL approaches still face three major issues: 1) How to propose a more efficient and secure privacy-preserving method; 2) How to verify the identity of participants to ensure they are not impersonators; 3) How to reduce the significant communication cost. To address the aforementioned concerns, several schemes have been proposed. However, these schemes suffer from flaws in security, efficiency, and functionality. Furthermore, few researches have considered the possibility of adversaries impersonating legitimate participants to undermine the integrity and availability of the model or launch a free-riding attack. In this paper, we first combine the advantages of secret sharing, Diffie-Hellman key agreement, and functional encryption to develop an authenticable secure multi-party computing algorithm (SDF-ASMC). This algorithm can guarantee the security of transmitted data and provide authentication functionality in the absence of a trusted third party. Moreover, an efficient, secure, and authenticable FL algorithm (ESAFL), which leverages compressed sensing and all-or-nothing transform, is introduced to reduce the transmission and encryption of local gradients. Then, only the final element of the transformed measurements is encrypted by our proposed SDF-ASMC to protect all the measurements. This method effectively improves the efficiency of our algorithm. In addition, ESAFL also tolerates participants’ dropout. Security analysis demonstrates that our proposed algorithms can securely aggregate local gradients. Finally, the extensive experiments demonstrate the practical performance of our proposed algorithms.
{"title":"Secure and Efficient Federated Learning via Novel Authenticable Multi-Party Computation and Compressed Sensing","authors":"Lvjun Chen;Di Xiao;Xiangli Xiao;Yushu Zhang","doi":"10.1109/TIFS.2024.3486611","DOIUrl":"10.1109/TIFS.2024.3486611","url":null,"abstract":"Federated learning (FL) facilitates collaborative training of a global model without sharing the participants’ raw data. Nevertheless, existing FL approaches still face three major issues: 1) How to propose a more efficient and secure privacy-preserving method; 2) How to verify the identity of participants to ensure they are not impersonators; 3) How to reduce the significant communication cost. To address the aforementioned concerns, several schemes have been proposed. However, these schemes suffer from flaws in security, efficiency, and functionality. Furthermore, few researches have considered the possibility of adversaries impersonating legitimate participants to undermine the integrity and availability of the model or launch a free-riding attack. In this paper, we first combine the advantages of secret sharing, Diffie-Hellman key agreement, and functional encryption to develop an authenticable secure multi-party computing algorithm (SDF-ASMC). This algorithm can guarantee the security of transmitted data and provide authentication functionality in the absence of a trusted third party. Moreover, an efficient, secure, and authenticable FL algorithm (ESAFL), which leverages compressed sensing and all-or-nothing transform, is introduced to reduce the transmission and encryption of local gradients. Then, only the final element of the transformed measurements is encrypted by our proposed SDF-ASMC to protect all the measurements. This method effectively improves the efficiency of our algorithm. In addition, ESAFL also tolerates participants’ dropout. Security analysis demonstrates that our proposed algorithms can securely aggregate local gradients. Finally, the extensive experiments demonstrate the practical performance of our proposed algorithms.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10141-10156"},"PeriodicalIF":6.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TIFS.2024.3486002
Jiawen Wu;Kai Zhang;Lifei Wei;Junqing Gong;Jianting Ning
Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive queries over joins of tables, while neglecting arbitrary Boolean queries with disjunctive and conjunctive normal forms (DNF/CNF) in TWINSSE (PETS 2023). However, trivially combining JXT and TWINSSE for arbitrary DNF/CNF boolean queries over joins of tables seems infeasible due to: (i) no support for dis/conjunctive query with the same meta-keyword; (ii) returning inaccurate search results; (iii) incurring costly storage overhead. Therefore, we introduce TNT-QJ, a practical TwiN cross-Tag protocol for arbitrary boolean Query-Join over multi-tables. The result is technically obtained from revisiting TWINSSE’s framework via using s-term (the least frequent keyword) for the relation between a keyword and its meta-keyword, and non-trivially combined with JXT’s query-join approach for introducing a connective attributed in encryption tuples. In addition, we present a semi-full multi-fork searchable tree to store keyword information and reveal keyword containment relations, where the storage consumption is reduced from $mathcal {O}(n^{3})$ to $mathcal {O}(n^{2})$ . Finally, to clarify practical performance, we conduct extensive experiments on JXT and TNT-QJ using an open database in the HUAWEI cloud. Besides enabling disjunctive queries over joins of tables, TNT-QJ also runs $1.2times $ faster for conjunctive queries than JXT (with #keywords=2), which confirms rich features and practical efficiency.
{"title":"Practical Searchable Symmetric Encryption for Arbitrary Boolean Query-Join in Cloud Storage","authors":"Jiawen Wu;Kai Zhang;Lifei Wei;Junqing Gong;Jianting Ning","doi":"10.1109/TIFS.2024.3486002","DOIUrl":"10.1109/TIFS.2024.3486002","url":null,"abstract":"Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive queries over joins of tables, while neglecting arbitrary Boolean queries with disjunctive and conjunctive normal forms (DNF/CNF) in TWINSSE (PETS 2023). However, trivially combining JXT and TWINSSE for arbitrary DNF/CNF boolean queries over joins of tables seems infeasible due to: (i) no support for dis/conjunctive query with the same meta-keyword; (ii) returning inaccurate search results; (iii) incurring costly storage overhead. Therefore, we introduce TNT-QJ, a practical TwiN cross-Tag protocol for arbitrary boolean Query-Join over multi-tables. The result is technically obtained from revisiting TWINSSE’s framework via using s-term (the least frequent keyword) for the relation between a keyword and its meta-keyword, and non-trivially combined with JXT’s query-join approach for introducing a connective attributed in encryption tuples. In addition, we present a semi-full multi-fork searchable tree to store keyword information and reveal keyword containment relations, where the storage consumption is reduced from \u0000<inline-formula> <tex-math>$mathcal {O}(n^{3})$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$mathcal {O}(n^{2})$ </tex-math></inline-formula>\u0000. Finally, to clarify practical performance, we conduct extensive experiments on JXT and TNT-QJ using an open database in the HUAWEI cloud. Besides enabling disjunctive queries over joins of tables, TNT-QJ also runs \u0000<inline-formula> <tex-math>$1.2times $ </tex-math></inline-formula>\u0000 faster for conjunctive queries than JXT (with #keywords=2), which confirms rich features and practical efficiency.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10086-10098"},"PeriodicalIF":6.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TIFS.2024.3486027
Kangkang Wei;Weiqi Luo;Jiwu Huang
Current deep learning-based steganalyzers often depend on specific image dimensions, leading to inevitable adjustments in network structure when dealing with varied image sizes. This impedes their effectiveness in managing the wide range of image sizes commonly found on social media. To address this issue, our paper presents a novel steganalytic network that is optimized for fixed-size (notably, $256times 256$ ) color images, but is capable of efficiently detecting stego images of arbitrary size without needing retraining or modifications to the network. Our proposed network is comprised of four modules. In the initial stem module, we calculate truncated residuals for each color channel of the input image. Diverging from existing steganalytic networks that rely on vanilla convolution, we have developed a pixel difference convolution module designed to better capture the artifacts introduced by steganography. Following this, we introduce an enhanced Transformer module with selective pooling, aimed at more effectively extracting global steganalytic features. To guarantee our network’s adaptability to different image sizes, we have developed a selective pooling strategy. This involves using global covariance pooling for fixed-size color images and spatial pyramid pooling for color images of various other sizes. This approach effectively standardizes the feature maps into uniform feature vectors. The final module is focused on classification. Extensive testing results on the ALASKA II color image dataset have demonstrated that our approach significantly improves detection performance for both fixed-size and arbitrary-size images, achieving state-of-the-art results. Additionally, we provide numerous ablation studies to confirm the effectiveness and soundness of our proposed network architecture.
目前基于深度学习的隐写分析器通常依赖于特定的图像尺寸，导致在处理不同尺寸的图像时，网络结构不可避免地要进行调整。这妨碍了它们管理社交媒体上常见的各种图像尺寸的有效性。为了解决这个问题，我们的论文提出了一种新型隐写网络，它针对固定尺寸（特别是 256times 256$）的彩色图像进行了优化，但能够高效检测任意尺寸的隐写图像，而无需对网络进行重新训练或修改。我们提出的网络由四个模块组成。在初始干模块中，我们计算输入图像每个颜色通道的截断残差。与现有的依靠虚假卷积的隐写网络不同，我们开发了一个像素差值卷积模块，旨在更好地捕捉隐写术带来的假象。在此基础上，我们引入了具有选择性池化功能的增强型变换器模块，旨在更有效地提取全局隐写特征。为了保证我们的网络能够适应不同大小的图像，我们开发了一种选择性汇集策略。这包括对固定尺寸的彩色图像使用全局协方差池，对其他各种尺寸的彩色图像使用空间金字塔池。这种方法能有效地将特征图标准化为统一的特征向量。最后一个模块的重点是分类。ALASKA II 彩色图像数据集的大量测试结果表明，我们的方法显著提高了固定尺寸和任意尺寸图像的检测性能，达到了最先进的效果。此外，我们还提供了大量的消融研究，以证实我们提出的网络架构的有效性和合理性。
{"title":"Color Image Steganalysis Based on Pixel Difference Convolution and Enhanced Transformer With Selective Pooling","authors":"Kangkang Wei;Weiqi Luo;Jiwu Huang","doi":"10.1109/TIFS.2024.3486027","DOIUrl":"10.1109/TIFS.2024.3486027","url":null,"abstract":"Current deep learning-based steganalyzers often depend on specific image dimensions, leading to inevitable adjustments in network structure when dealing with varied image sizes. This impedes their effectiveness in managing the wide range of image sizes commonly found on social media. To address this issue, our paper presents a novel steganalytic network that is optimized for fixed-size (notably, \u0000<inline-formula> <tex-math>$256times 256$ </tex-math></inline-formula>\u0000) color images, but is capable of efficiently detecting stego images of arbitrary size without needing retraining or modifications to the network. Our proposed network is comprised of four modules. In the initial stem module, we calculate truncated residuals for each color channel of the input image. Diverging from existing steganalytic networks that rely on vanilla convolution, we have developed a pixel difference convolution module designed to better capture the artifacts introduced by steganography. Following this, we introduce an enhanced Transformer module with selective pooling, aimed at more effectively extracting global steganalytic features. To guarantee our network’s adaptability to different image sizes, we have developed a selective pooling strategy. This involves using global covariance pooling for fixed-size color images and spatial pyramid pooling for color images of various other sizes. This approach effectively standardizes the feature maps into uniform feature vectors. The final module is focused on classification. Extensive testing results on the ALASKA II color image dataset have demonstrated that our approach significantly improves detection performance for both fixed-size and arbitrary-size images, achieving state-of-the-art results. Additionally, we provide numerous ablation studies to confirm the effectiveness and soundness of our proposed network architecture.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"9970-9983"},"PeriodicalIF":6.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TIFS.2024.3486098
Yan He;Fei Peng;Rizhao Cai;Zitong Yu;Min Long;Kwok-Yan Lam
In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and target domains. To this end, this work develops a category-conditional gradient alignment guided face anti-spoofing algorithm (CCGA-FAS) from a novel perspective of gradient discrepancy elimination. Technically, the category-conditional gradient alignment mechanism maximizes the cosine similarity of the gradient vectors generated by source and target samples within the live and spoof categories separately, which promotes the source and target domains to follow similar gradient descent directions during optimization. Considering that the gradient vector generation and alignment is computationally dependent on reliable category information, a temporal knowledge and flexible threshold based dynamic category measurer is devised to provide pseudo category information for unlabelled target samples in an easy-to-hard manner. The optimization for CCGA-FAS is implemented under the teacher-student structure, where the student model serves as the gradient optimization backbone, and the category prediction simultaneously benefits from the teacher and student models to consolidate the alignment stability. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods in both unsupervised and K-shot semi-supervised domain adaptive face anti-spoofing scenarios.
{"title":"Category-Conditional Gradient Alignment for Domain Adaptive Face Anti-Spoofing","authors":"Yan He;Fei Peng;Rizhao Cai;Zitong Yu;Min Long;Kwok-Yan Lam","doi":"10.1109/TIFS.2024.3486098","DOIUrl":"10.1109/TIFS.2024.3486098","url":null,"abstract":"In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and target domains. To this end, this work develops a category-conditional gradient alignment guided face anti-spoofing algorithm (CCGA-FAS) from a novel perspective of gradient discrepancy elimination. Technically, the category-conditional gradient alignment mechanism maximizes the cosine similarity of the gradient vectors generated by source and target samples within the live and spoof categories separately, which promotes the source and target domains to follow similar gradient descent directions during optimization. Considering that the gradient vector generation and alignment is computationally dependent on reliable category information, a temporal knowledge and flexible threshold based dynamic category measurer is devised to provide pseudo category information for unlabelled target samples in an easy-to-hard manner. The optimization for CCGA-FAS is implemented under the teacher-student structure, where the student model serves as the gradient optimization backbone, and the category prediction simultaneously benefits from the teacher and student models to consolidate the alignment stability. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods in both unsupervised and K-shot semi-supervised domain adaptive face anti-spoofing scenarios.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10071-10085"},"PeriodicalIF":6.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/tifs.2024.3484946
Liang Xie, Zhou Su, Yuntao Wang, Zhendong Li
{"title":"A Practical Federated Learning Framework with Truthful Incentive in UAV-Assisted Crowdsensing","authors":"Liang Xie, Zhou Su, Yuntao Wang, Zhendong Li","doi":"10.1109/tifs.2024.3484946","DOIUrl":"https://doi.org/10.1109/tifs.2024.3484946","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"235 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TIFS.2024.3484950
Liang Guo;Jie Jia;Jian Chen;Shuhui Yang;Xingwei Wang
Integrated sensing and communication (ISAC) has been regarded as an emerging technique to satisfy the sensing requirements for future 6G networks. However, the confidential communication information embedded in the probing waveform could be eavesdropped by the radar targets, which leads to insecurity issues for ISAC systems. To this end, we propose a coordinated multi-point transmission (CoMP) empowered secure ISAC system. Unlike existing work focusing on a single base station (BS), multiple BSs are coordinated to improve sensing performance and communication security. Specifically, non-orthogonal multiple access (NOMA) is employed to improve spectrum efficiency and facilitate spectrum sharing between sensing and communication functions. By importing the artificial noise (AN) to disrupt eavesdropper reception, a joint radar association and beamforming design optimization problem is formulated to maximize the minimum beampattern gain, subject to the maximum power constraint and secure communication requirements. The mixed-integer non-convex optimization problem is first transformed into more tractable forms. Then, a near-optimal solution is obtained by applying an accelerated stochastic coordinate descent algorithm for radar association and the penalty-based iterative algorithm for beamforming design. Moreover, the optimization problem is further extended to more practical cases with uncertain target directions. Our numerical results show: i) the proposed AN-aided COMP-NOMA empowered ISAC system can support much higher high-quality radar sensing, while simultaneously guaranteeing secure communication; ii) the proposed scheme significantly outperforms the relevant benchmark schemes in terms of the beampattern gain; iii) the proposed joint optimization algorithm can achieve high beampattern gain, even with uncertain target directions.
{"title":"Secure Beamforming and Radar Association in CoMP-NOMA Empowered Integrated Sensing and Communication Systems","authors":"Liang Guo;Jie Jia;Jian Chen;Shuhui Yang;Xingwei Wang","doi":"10.1109/TIFS.2024.3484950","DOIUrl":"10.1109/TIFS.2024.3484950","url":null,"abstract":"Integrated sensing and communication (ISAC) has been regarded as an emerging technique to satisfy the sensing requirements for future 6G networks. However, the confidential communication information embedded in the probing waveform could be eavesdropped by the radar targets, which leads to insecurity issues for ISAC systems. To this end, we propose a coordinated multi-point transmission (CoMP) empowered secure ISAC system. Unlike existing work focusing on a single base station (BS), multiple BSs are coordinated to improve sensing performance and communication security. Specifically, non-orthogonal multiple access (NOMA) is employed to improve spectrum efficiency and facilitate spectrum sharing between sensing and communication functions. By importing the artificial noise (AN) to disrupt eavesdropper reception, a joint radar association and beamforming design optimization problem is formulated to maximize the minimum beampattern gain, subject to the maximum power constraint and secure communication requirements. The mixed-integer non-convex optimization problem is first transformed into more tractable forms. Then, a near-optimal solution is obtained by applying an accelerated stochastic coordinate descent algorithm for radar association and the penalty-based iterative algorithm for beamforming design. Moreover, the optimization problem is further extended to more practical cases with uncertain target directions. Our numerical results show: i) the proposed AN-aided COMP-NOMA empowered ISAC system can support much higher high-quality radar sensing, while simultaneously guaranteeing secure communication; ii) the proposed scheme significantly outperforms the relevant benchmark schemes in terms of the beampattern gain; iii) the proposed joint optimization algorithm can achieve high beampattern gain, even with uncertain target directions.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10246-10257"},"PeriodicalIF":6.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TIFS.2024.3484666
Jiahong Wu;Nan Liu;Wei Kang
We study the problem of distributed multi-user secret sharing (DMUSS), involving a main node, N storage nodes, and K users. Every user has access to the contents of a certain subset of storage nodes and wants to decode an independent secret message. With knowledge of K secret messages, the main node strategically places encoded shares in the storage nodes, ensuring two crucial conditions: (i) each user can recover its own secret message from the storage nodes that it has access to; (ii) each user is unable to acquire any information regarding the collection of $K-1$ secret messages for all the other users. The rate of each user is defined as the size of its secret message normalized by the size of a storage node. We characterize the capacity region of the DMUSS problem, which is the closure of the set of all achievable rate tuples that satisfy the correctness and perfect secrecy conditions. The converse proof relies on a bound from the traditional single-secret sharing regime. In the achievability proof, we firstly design the linear decoding functions, based on the fact that each secret message needs to be recovered from a single set of storage nodes. It turns out that the perfect secrecy condition holds if K matrices, whose entries are extracted from the decoding functions, are full rank. We prove that the decoding functions can be constructed explicitly if the rate tuple satisfies the converse and the field size is not less than K. At last, the encoding functions are obtained by solving the system of linear decoding functions, where some shares are equal to the randomness and the other shares are linear combinations of the secret messages and the randomness.
我们研究的分布式多用户秘密共享（DMUSS）问题涉及一个主节点、N 个存储节点和 K 个用户。每个用户都能访问某个存储节点子集的内容，并希望解码一个独立的密文。在知道 K 个密文后，主节点会有策略地将编码共享放到存储节点中，以确保两个关键条件：(i) 每个用户都能从其可访问的存储节点中恢复自己的密文；(ii) 每个用户都无法获得有关所有其他用户的 $K-1$ 密文集合的任何信息。每个用户的速率定义为其秘密信息的大小与存储节点大小的归一化。我们描述了 DMUSS 问题的容量区域，即满足正确性和完全保密条件的所有可实现速率图元集合的闭合区域。反向证明依赖于传统的单秘密共享机制的约束。在可实现性证明中，我们首先设计了线性解码函数，其依据是每条秘密信息都需要从一组存储节点中恢复。结果表明，如果从解码函数中提取的 K 矩阵的条目是满级的，则完美保密条件成立。最后，我们通过求解线性解码函数系统得到了编码函数，其中一些份额等于随机性，另一些份额是密文和随机性的线性组合。
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Pub Date : 2024-10-23DOI: 10.1109/TIFS.2024.3484661
Shinimol Salim;Waquar Ahmad
Patients with dysarthria and physical impairments face challenges with traditional user interfaces. An Automatic Speaker Verification (ASV) system can enhance accessibility by replacing complex authentication methods and enabling voice biometrics in various applications for patients with dysarthria. This study focuses on enhancing accessibility of patients with dysarthria through an ASV system. In this study, a noval low variance Multitaper Linear Frequency Cepstral Coefficients (MTLFCC) feature is proposed. An ASV system for patients with dysarthria is implemented using the voice conversion data augmentation within a DNN framework. An extensive analysis is conducted to compare various multitaper techniques and taper weight choices using the Thomson multitaper method, specifically verifying patients with dysarthria as speakers. The impact of voice conversion through a cycle-consistent generative adversarial network (Cycle GAN) is also examined by modifying the acoustic attributes of control speech to make it perceptually similar to dysarthria speech and its implications for dysarthria ASV. Furthermore, the system performance is analyzed for different severity level of dysarthria to gain insight into how the selected multitaper parameters influence the outcomes. This study pioneers the use of MTLFCC features for ASV in the context of dysarthria, offering a novel approach to improve accessibility for this group.
有构音障碍和肢体障碍的患者面临着传统用户界面的挑战。自动语音验证（ASV）系统可以取代复杂的身份验证方法，在各种应用中使用语音生物识别技术，从而提高构音障碍患者的无障碍程度。本研究的重点是通过 ASV 系统提高构音障碍患者的无障碍性。在这项研究中，我们提出了一种新的低方差多锥体线性频率倒频谱系数（MTLFCC）特征。利用 DNN 框架内的语音转换数据增强功能，为构音障碍患者实现了 ASV 系统。通过广泛的分析，比较了各种多锥度技术和使用汤姆森多锥度方法的锥度权重选择，特别是将构音障碍患者作为扬声器进行验证。还通过修改控制语音的声学属性，使其在感知上与构音障碍语音相似，研究了通过循环一致性生成对抗网络（Cycle GAN）进行语音转换的影响及其对构音障碍 ASV 的影响。此外，还对不同严重程度的构音障碍进行了系统性能分析，以深入了解所选多合成参数对结果的影响。这项研究开创性地将 MTLFCC 特征用于构音障碍 ASV，为改善该群体的无障碍环境提供了一种新方法。
{"title":"Advancing Voice Biometrics for Dysarthria Speakers Using Multitaper LFCC and Voice Conversion Data Augmentation","authors":"Shinimol Salim;Waquar Ahmad","doi":"10.1109/TIFS.2024.3484661","DOIUrl":"10.1109/TIFS.2024.3484661","url":null,"abstract":"Patients with dysarthria and physical impairments face challenges with traditional user interfaces. An Automatic Speaker Verification (ASV) system can enhance accessibility by replacing complex authentication methods and enabling voice biometrics in various applications for patients with dysarthria. This study focuses on enhancing accessibility of patients with dysarthria through an ASV system. In this study, a noval low variance Multitaper Linear Frequency Cepstral Coefficients (MTLFCC) feature is proposed. An ASV system for patients with dysarthria is implemented using the voice conversion data augmentation within a DNN framework. An extensive analysis is conducted to compare various multitaper techniques and taper weight choices using the Thomson multitaper method, specifically verifying patients with dysarthria as speakers. The impact of voice conversion through a cycle-consistent generative adversarial network (Cycle GAN) is also examined by modifying the acoustic attributes of control speech to make it perceptually similar to dysarthria speech and its implications for dysarthria ASV. Furthermore, the system performance is analyzed for different severity level of dysarthria to gain insight into how the selected multitaper parameters influence the outcomes. This study pioneers the use of MTLFCC features for ASV in the context of dysarthria, offering a novel approach to improve accessibility for this group.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10114-10129"},"PeriodicalIF":6.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, phishing scams have emerged as one of the most serious crimes on Ethereum. Existing phishing scam detection methods typically model public transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis. Therefore, these methods lack the ability to capture fine-grained time dynamics, and on the other hand, they cannot handle the large-scale and continuously growing transaction data on the Ethereum blockchain, resulting in lower scalability and efficiency. In this paper, we propose a two-dimensional streaming framework 2DynEthNet for Ethereum phishing scam detection. First, we cast the transaction series into 6 slices according to block numbers, treating each as a separate task. In the first dimension, we treat transaction features as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. In the second dimension, we adopt the strategy of incremental information training between tasks, which utilizes meta-learning to quickly update the model parameters under new slices, thus effectively improving the scalability of the model. Finally, experimental results on large-scale real Ethereum phishing scam datasets show that our 2DynEthNet outperforms the state-of-the-art methods with 28.44% average Recall and achieves the most efficient training speed, proving the effectiveness of both temporal edge representation and meta-learning. In addition, we provide an Ethereum large-scale dynamic graph transaction dataset, ETGraph, which aligns with the data distribution in real transaction scenarios without sampling and filtering unlabeled accounts.
{"title":"2DynEthNet: A Two-Dimensional Streaming Framework for Ethereum Phishing Scam Detection","authors":"Jingjing Yang;Wenjia Yu;Jiajing Wu;Dan Lin;Zhiying Wu;Zibin Zheng","doi":"10.1109/TIFS.2024.3484296","DOIUrl":"10.1109/TIFS.2024.3484296","url":null,"abstract":"In recent years, phishing scams have emerged as one of the most serious crimes on Ethereum. Existing phishing scam detection methods typically model public transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis. Therefore, these methods lack the ability to capture fine-grained time dynamics, and on the other hand, they cannot handle the large-scale and continuously growing transaction data on the Ethereum blockchain, resulting in lower scalability and efficiency. In this paper, we propose a two-dimensional streaming framework 2DynEthNet for Ethereum phishing scam detection. First, we cast the transaction series into 6 slices according to block numbers, treating each as a separate task. In the first dimension, we treat transaction features as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. In the second dimension, we adopt the strategy of incremental information training between tasks, which utilizes meta-learning to quickly update the model parameters under new slices, thus effectively improving the scalability of the model. Finally, experimental results on large-scale real Ethereum phishing scam datasets show that our 2DynEthNet outperforms the state-of-the-art methods with 28.44% average Recall and achieves the most efficient training speed, proving the effectiveness of both temporal edge representation and meta-learning. In addition, we provide an Ethereum large-scale dynamic graph transaction dataset, ETGraph, which aligns with the data distribution in real transaction scenarios without sampling and filtering unlabeled accounts.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"9924-9937"},"PeriodicalIF":6.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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