IEEE Transactions on Information Forensics and Security最新文献

{"title":"TFSCL: A Novel Time-Frequency Similarity Contrastive Learning Method With Hybrid Augmentation for Robust and Accurate Specific Emitter Identification","authors":"Kaixuan Huang;Yongtao Ma;Jialu Zhu;Yuxiang Han","doi":"10.1109/TIFS.2026.3671097","DOIUrl":"10.1109/TIFS.2026.3671097","url":null,"abstract":"As the Internet of Things (IoT) and Sixth Generation (6G) technologies advance rapidly, the cryptographic identification of electronic devices has become a critical issue in information security. Radio frequency fingerprint (RFF)-based specific emitter identification (SEI) has emerged as a prominent physical-layer authentication technique. To enhance the stability and accuracy of multi-target recognition in complex electromagnetic environments, a novel technique for specific emitter identification based on time-frequency similarity contrastive learning (TFSCL) is proposed. In this study, we present a novel pre-training method utilizing a deep complex-valued pyramid network (DCPN) to enhance the extraction and reconstruction of time series and frequency domain sequences. The DCPN enables contrastive learning of signal features in both the temporal and frequency domains, significantly reducing computational complexity and improving pretraining performance. Additionally, we first introduce the Time-Frequency Synchronization Data Added (TFS-DA), a Time-Frequency Hybrid Data Added Technique that employs Gray code to generate random sequences, effectively improving feature representation in both domains. Empirical results demonstrate that the proposed method achieves an accuracy rate of 97.12% on an automatic-dependent surveillance-broadcast (ADS-B) dataset that contains 10 categories with only data labeled 10%. On a LoRa dataset containing 30 categories with only data labeled 10%, the accuracy rate reaches 77.06%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2668-2681"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371545","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}

{"title":"Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression","authors":"Xian Qin;Xue Yang;Xiaohu Tang","doi":"10.1109/TIFS.2026.3671104","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3671104","url":null,"abstract":"Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encryption with dimension compression based on the Johnson-Lindenstrauss transformation. Our approach employs a dual-server architecture that enables secure Byzantine defense in the ciphertext domain while dramatically reducing computational overhead through gradient compression. The dimension compression technique preserves the geometric relationships necessary for Byzantine defence while reducing computation complexity from <inline-formula> <tex-math>$O(dn)$ </tex-math></inline-formula> to <inline-formula> <tex-math>$O(kn)$ </tex-math></inline-formula> cryptographic operations, where <inline-formula> <tex-math>$k ll d$ </tex-math></inline-formula>. Extensive experiments across diverse datasets demonstrate that our approach maintains model accuracy comparable to non-private FL while effectively defending against Byzantine clients comprising up to 40% of the network. Our approach also demonstrates substantial improvements in computational and communication efficiency. Experimental evaluation shows that the dimension compression technique achieves <inline-formula> <tex-math>$25 times sim 35 times $ </tex-math></inline-formula> reduction in computational overhead and <inline-formula> <tex-math>$17 times $ </tex-math></inline-formula> reduction in communication overhead compared to our non-compression version. When compared to state-of-the-art methods like ShieldFL, our approach demonstrates order-of-magnitude improvements in both computational and communication efficiency while maintaining equivalent privacy guarantees and achieving superior Byzantine robustness comparable to FLTrust. These substantial efficiency enhancements make secure FL practical for deployment in large-scale neural networks with millions of parameters.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2596-2609"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440573","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}

{"title":"Bi-Level Inter-Modality Modulation for Unsupervised Visible-Infrared Person Re-Identification","authors":"Jinjia Peng;Junyu Liu;Xutao Zuo;Zeze Tao;Huibing Wang","doi":"10.1109/TIFS.2026.3671087","DOIUrl":"10.1109/TIFS.2026.3671087","url":null,"abstract":"The task of unsupervised visible–infrared person re-identification (USL-VI-ReID) aims to retrieve cross-modal pedestrian images without manual annotations. The key challenge lies in achieving semantic alignment to resolve modality bias in the absence of real labels. However, existing methods overly rely on single-modal information in the process of pseudo-label generation without considering cross-modal associations, making it difficult to bridge the modality gap between visible and infrared images. To address these issues, this paper proposes a Bi-level Inter-Modal Modulation Network (BIMM-Net), which employs multi-level cluster structure optimization as a core strategy to drive the establishment of cross-modal semantic associations, ultimately achieving cross-modal alignment at the feature representation level. Specifically, we construct a novel intermediary modality GrayMix from visible images to enhance model robustness against color variations and alleviate modality gaps. To filter out noise in cross-modal matching and establish a shared semantic space between visible and infrared modalities, we further develop a Ternary Pairs Calibration-Convergence module designed for filtering noise from visible-infrared cluster matching, on this basis constructing fused mixture clusters. Building on this mixture cluster space, an Heterogeneous-Isomorphic Alignment Loss is also designed to align the feature distributions of the three modalities, reinforcing cross-modal semantic consistency. In addition, we present a Cross-modal Neighborhood Consistency Clustering method, which facilitates the formation and propagation of cross-modal clusters by selecting high-confidence cross-modal neighbor pairs and refining feature distances. Ultimately, BIMM-Net through the joint modeling of bi-level clustering enables multiple levels to guide each other in refining cross-modal structures, thereby effectively establishing the semantic associations between visible and infrared modalities. Extensive experiments validate the superior performance of the proposed framework, achieving state-of-the-art results in USL-VI-ReID. The source code of this paper is available at: <uri>https://github.com/liujuny5920/DIMM-Net</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2652-2667"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371542","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}

{"title":"Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment","authors":"Yuteng Wang;Zhenxin Cai;Tiantian Tang;Tomoaki Ohtsuki;Guan Gui","doi":"10.1109/TIFS.2026.3671107","DOIUrl":"10.1109/TIFS.2026.3671107","url":null,"abstract":"With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2641-2651"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371509","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}

{"title":"OwnerHunter: Multilingual Website Owner Identification Powered by Large Language Model","authors":"Cheng Tu;Enhuan Dong;Yunyi Zhang;Shiyu Wang;Zexiang Zhang;Min Zhang;Yang Li;Pengfei Xue;Jiahai Yang","doi":"10.1109/TIFS.2026.3666852","DOIUrl":"10.1109/TIFS.2026.3666852","url":null,"abstract":"As cyberspace continues to expand, identifying the organization or individual behind a website has become increasingly vital in security incident response, phishing website detection, and other cybersecurity subfields. An existing solution for it involves analyzing webpage content and extracting owner names using named entity recognition techniques. However, since these techniques operate on a sentence-by-sentence basis, they struggle to identify the true owner when multiple individual or organizational names appear on a webpage. Moreover, they often perform poorly on non-English websites. To address these limitations, we propose OwnerHunter,<xref>1</xref> a novel multilingual framework powered by large language models, which formulates website owner identification as a multilingual document-level information extraction task and utilizes global information from webpages to identify the owner. In OwnerHunter, we first craft prompts that fully leverage the capabilities of large language models to effectively recognize potential owners on webpages in different languages with minimal examples. To enhance the comprehensiveness and accuracy of recognition, we further design a multimodal augmentation strategy, an example pool strategy, and a self-verification strategy. Then, we devise a semantic and string similarity aggregation-based entity disambiguation technique to eliminate ambiguities among multiple potential owners recognized by large language models and a position-based hybrid ranking technique to exactly select the true owner. To evaluate OwnerHunter, we refine the publicly available English dataset ONER and construct the Chinese dataset WOI-cn with 16,036 real websites. Experimental results show that OwnerHunter achieves F1 scores of 0.9505 on ONER and 0.9621 on WOI-cn, setting new state-of-the-art performance on both datasets.<fn><label>¹</label><p>Codes are available at <uri>https://github.com/tuchen9/OwnerHunter</uri></p></fn>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2430-2445"},"PeriodicalIF":8.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350973","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}

{"title":"Cert-SSBD: Certified Backdoor Defense With Sample-Specific Smoothing Noises","authors":"Ting Qiao;Yingjia Wang;Xing Liu;Sixing Wu;Jianbin Li;Yiming Li","doi":"10.1109/TIFS.2026.3667460","DOIUrl":"10.1109/TIFS.2026.3667460","url":null,"abstract":"Deep neural networks (DNNs) are vulnerable to backdoor attacks, where an attacker manipulates a small portion of the training data to implant hidden backdoors into the model. The compromised model behaves normally on clean samples but misclassifies backdoored samples into the attacker-specified target class, posing a significant threat to real-world DNN applications. Currently, several empirical defense methods have been proposed to mitigate backdoor attacks, but they are often bypassed by more advanced backdoor techniques. In contrast, certified defenses based on randomized smoothing have shown promise by adding random noise to training and testing samples to counteract backdoor attacks. In this paper, we reveal that existing randomized smoothing defenses implicitly assume that all samples are equidistant from the decision boundary. However, it may not hold in practice, leading to suboptimal certification performance. To address this issue, we propose a certified backdoor defense method with sample-specific smoothing noises, termed Cert-SSBD. Cert-SSBD first employs stochastic gradient ascent to optimize the noise magnitude for each sample, ensuring a sample-specific noise level that is then applied to multiple poisoned training sets to retrain several smoothed models. After that, Cert-SSBD aggregates the predictions of multiple smoothed models to generate the final robust prediction. In particular, in this case, existing certification methods become inapplicable since the optimized noise varies across different samples. To conquer this challenge, we introduce a storage-update-based certification method, which dynamically adjusts each sample’s certification region to improve certification performance. We conduct extensive experiments on multiple benchmark datasets, demonstrating the effectiveness of our proposed method. Our code is available at <uri>https://github.com/NcepuQiaoTing/Cert-SSBD</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2446-2461"},"PeriodicalIF":8.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147279427","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}

{"title":"VoIP Call Identification via a Dual-Level 1D-CNN With Frame and Utterance Features","authors":"Guoyuan Lin;Weiqi Luo;Peijia Zheng;Jiwu Huang","doi":"10.1109/TIFS.2026.3667459","DOIUrl":"10.1109/TIFS.2026.3667459","url":null,"abstract":"The increasing use of Voice over Internet Protocol (VoIP) technology in telecom fraud has become a serious global concern. Its ability to spoof caller IDs and IP addresses, and the use of overseas or anonymized servers make VoIP-based scams difficult to trace and regulate. As a result, distinguishing VoIP calls from conventional mobile phone calls based on voice signal characteristics is crucial for enhancing anti-fraud measures. However, existing forensic techniques often struggle to accurately identify speech transmitted via VoIP. To address this challenge, we propose a dual-level 1D-CNN that leverages both frame and utterance features for effective VoIP detection. After evaluating a range of acoustic features, we primarily focus on short-frame Mel-Frequency Cepstral Coefficients (MFCCs) due to their effectiveness in capturing VoIP characteristics. Given the frame-based processing and transmission nature of VoIP, we employ a 1D-CNN, rather than the more commonly used 2D-CNN that treats spectrograms as image, to extract frame-level codec features. Finally, we propose a dual-level classification strategy: the frame-level classifier captures encoding discrepancies within individual frames, while the utterance-level classifier aggregates these frame-level features to learn global encoding patterns through global covariance pooling. Experimental results on the VoIP Phone Call Identification Database (VPCID) demonstrate that the proposed method consistently outperforms existing approaches, delivering superior accuracy and robustness across a wide range of challenging scenarios. Moreover, comprehensive ablation studies validate the effectiveness and rationale behind the design of the proposed model architecture.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2389-2402"},"PeriodicalIF":8.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147279429","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}

{"title":"Secure and Efficient Model Training Framework for Multiuser Semantic Communications via Over-the-Air Mixup","authors":"Xun Ma;Xinchen Lyu;Chenshan Ren;Guoshun Nan;Qimei Cui","doi":"10.1109/TIFS.2026.3666908","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3666908","url":null,"abstract":"Online model training is pivotal for enabling multiuser semantic communication systems to adapt to dynamic channel conditions. However, conventional frameworks suffer from prohibitive communication overhead and vulnerabilities to privacy attacks, hindering practical deployment. This paper proposes semantic information mixup (SIMix), a secure and efficient training framework that integrates Over-the-Air Mixup (OAM) with label-aware user grouping to jointly optimize spectral efficiency and semantic security. The OAM mixes semantic features of multiple users via wireless channels, inherently obfuscating sensitive data while reducing communication overhead. A closed-form Tx-Rx scaling optimization minimizes the mean square error (MSE) of over-the-air computation under channel noise, ensuring stable convergence in low-SNR regimes. Furthermore, an extended max-clique algorithm dynamically partitions users into groups with minimal intra-label similarity, reducing model inversion attack success rates. Experiments on CIFAR-10 and Tiny ImageNet demonstrate that the proposed approach is superior in terms of communication efficiency and security, reducing communication overhead by up to 25% and attaining 17.58 dB PSNR (20.98 dB reduction) under inversion attack and reducing 13.44% attack success rate under label inference attack, while achieving comparable transmission accuracy.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2358-2372"},"PeriodicalIF":8.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299681","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}

{"title":"Decoupled and Privacy-Preserving Key Generation in ABE Under the Minimal Disclosure Principle","authors":"Zhiqiang Zhang;Youwen Zhu;Xiaodong Yang;Xiaohui Ding;Changhee Hahn;Jian Wang;Junbeom Hur","doi":"10.1109/TIFS.2026.3666910","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3666910","url":null,"abstract":"Attribute-Based Encryption (ABE) enables fine-grained access control over outsourced data, but its key generation process typically requires users to disclose their complete attribute sets, introducing significant privacy risks. Existing privacy-preserving approaches—such as those based on zero-knowledge proofs or tightly coupled interactive protocols—suffer from limited scalability, high communication costs, and insufficient support for selective attribute disclosure. To address these limitations, we propose a privacy-enhancing key generation protocol guided by the principle of Minimal Disclosure, which ensures that users disclose only the minimally necessary subset of attributes required for authorization. Our protocol decouples attribute verification from key issuance: users first obtain cryptographically verifiable attribute tokens, and later issue blinded key requests over selectively chosen attributes. This design enables selective disclosure, supports reusable attribute credentials, and enhances user autonomy. To improve scalability, we introduce a lightweight batch verification mechanism that reduces computation and communication overhead for the attribute authority. We prove that our protocol achieves the binding and hiding properties under standard cryptographic assumptions, and we formally verify these guarantees in the symbolic model using the ProVerif tool. In addition, we propose two privacy metrics—Attribute Inference Gain (AIG) and Privacy Gain (PG)—alongside an entropy-based analysis to quantify resistance against attribute inference attacks. Experimental results show that our scheme effectively mitigates inference leakage while offering substantial efficiency gains compared to existing schemes.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2478-2491"},"PeriodicalIF":8.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362497","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}

{"title":"PPOM-Attack: A Substitute Model-Free Perturbation Prediction and Optimization Method for Black-Box Adversarial Attack Against Face Recognition","authors":"Ke Cheng;Jixin Zhang;Haiyun Li;Zipeng Zhong;Mingwu Zhang;Zheng Qin","doi":"10.1109/TIFS.2026.3666853","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3666853","url":null,"abstract":"Face recognition (FR) brings convenience to people’s lives while also posing security risks. Some malicious users employ FR attacks to impersonate the identity of a target. To reveal the security risks, recent work has attacked black-box FR models by utilizing substitute models to generate adversarial face images that are misclassified as the target individual due to the attack transferability of substitute models. However, the substitute models cannot accurately approximate the target model that leads to a decrease in FR attack success rate and adversarial face image quality. To address the issue, we propose the <sc>PPOM-Attack</small>, a substitute model-free Perturbation Prediction and Optimization Method for black-box adversarial Attack against face recognition. <sc>PPOM-Attack</small> directly obtains feedback from the target model instead of using substitute models, it avoids any discrepancy with the attack objective. To achieve this goal, we design a proximal policy optimization (PPO)-based agent to predict the perturbation regions in the face image and self-adaptively disturb the regions. To maintain high-quality adversarial face images, we further propose a minimum brightness offsets method specifically designed to generate perturbations that minimize the feature embedding difference between the adversarial and targeted face images. The experimental results show that our approach outperforms state-of-the-art FR attack methods by an average of 21.7% in terms of attack success rate, while achieving better image quality on seven FR models.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2580-2595"},"PeriodicalIF":8.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440514","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}