IEEE Transactions on Information Forensics and Security最新文献

{"title":"ISFL-AE: Insider-Specific Feature Learning Autoencoder for Lightweight Insider Threat Detection","authors":"Yujun Kim;Young-Gab Kim","doi":"10.1109/TIFS.2026.3659000","DOIUrl":"10.1109/TIFS.2026.3659000","url":null,"abstract":"Malicious insiders who possess system access and security expertise are notoriously difficult to detect and can inflict severe financial damage. While recent advances in deep learning have demonstrated impressive accuracy in detecting insider threats, these models often assume the presence of well-defined or previously known anomalies. In practical organizational environments, however, threats may manifest as novel, subtle, or context-dependent behaviors that are not captured by existing patterns. Detecting such anomalies necessitates the extraction and analysis of rich behavioral features from large-scale insider activity data—an approach that, while effective, often leads to increased model complexity and computational burden. This, in turn, impedes real-time responsiveness and operational viability, potentially resulting in delayed threat mitigation and financial losses. Therefore, there is a pressing need for lightweight yet robust insider threat detection frameworks that can ensure timely and efficient deployment without compromising detection performance. To address this challenge, this paper proposes the Insider-Specific Feature Learning Autoencoder (ISFL-AE), a model designed to achieve high detection accuracy and fast processing speed. Unlike traditional reconstruction-based anomaly detection models—which use a single set of model parameters to reconstruct normal behavior for all insiders regardless of their role, authority level, or other attributes—ISFL-AE tailors its feature learning to insider-specific characteristics. ISFL-AE operates with the same number of parameters as a conventional autoencoder (AE), maintaining comparable processing speed while significantly improving detection performance. We evaluated ISFL-AE using the CERT r4.2 and r6.2 datasets. The results show that, while processing data at the same speed as a standard AE, ISFL-AE delivered markedly higher detection accuracy. Furthermore, it outperformed other machine learning models in detection accuracy and processing speed. Furthermore, our empirical results demonstrate that integrating insider-specific feature learning into autoencoder-based deep learning architectures significantly enhances anomaly detection performance, all while preserving real-time processing efficiency.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1638-1653"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070464","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":"Data Flipping Attack and Defense in Web Edge Caching Systems","authors":"Mengsha Kou;Xiaoyu Xia;Ibrahim Khalil;Ziqi Wang;Xiuzhen Zhang;Lin Yao;Minhui Xue","doi":"10.1109/TIFS.2026.3663056","DOIUrl":"10.1109/TIFS.2026.3663056","url":null,"abstract":"Caching web data on edge servers has become a common practice in latency-sensitive services to minimize data retrieval delays for web users. However, the geographic distribution of edge servers and frequent data transmissions make these systems vulnerable to security threats, particularly cache pollution attacks (CPAs). In such attacks, malicious users send excessive requests for unpopular data at abnormal frequencies, causing irrelevant content to be cached and degrading the system’s performance. Traditional CPAs, though impactful in conventional caching systems, are less effective in edge environments where user requests are more diverse and edge servers collaborate in caching strategies. In this paper, we identify a novel attack named data flipping attack (DFA) that targets the data transmission process among edge servers. This attack manipulates request distribution by swapping the frequencies of popular and unpopular data requests, all while maintaining other characteristics like request timing and user identity. This tactic disrupts caching strategies without raising suspicion. Experimental results indicate DFA is independent of user request patterns and demonstrates substantial effectiveness and robustness, successfully forcing edge web users to retrieve data from the cloud across various scales and configurations of edge networks. Furthermore, it evades detection by state-of-the-art methods that rely on specific distribution patterns, such as the Zipf distribution. To counter this attack, we propose an effective defense method that alters the request distribution by frequency distillation, mitigating its impact.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2505-2519"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160269","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":"Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition","authors":"Weizhe Kong;Xiao Wang;Ruichong Gao;Chenglong Li;Yu Zhang;Xing Yang;Yaowei Wang;Jin Tang","doi":"10.1109/TIFS.2026.3673132","DOIUrl":"10.1109/TIFS.2026.3673132","url":null,"abstract":"Pedestrian Attribute Recognition (PAR) is an indispensable task in human-centered research and has made great progress in recent years with the development of deep neural networks. However, the potential vulnerability and anti-interference ability have still not been fully explored. To bridge this gap, this paper proposes the first adversarial attack and defense framework for pedestrian attribute recognition. Specifically, we exploit both global- and patch-level attacks on the pedestrian images, based on the pre-trained CLIP-based PAR framework. It first divides the input pedestrian image into non-overlapping patches and embeds them into feature embeddings using a projection layer. Meanwhile, the attribute set is expanded into sentences using prompts and embedded into attribute features using a pre-trained CLIP text encoder. A multi-modal Transformer is adopted to fuse the obtained vision and text tokens, and a feed-forward network is utilized for attribute recognition. Based on the aforementioned PAR framework, we adopt the adversarial semantic and label-perturbation to generate the adversarial noise, termed ASL-PAR. We also design a semantic offset defense strategy to suppress the influence of adversarial attacks. Extensive experiments conducted on both digital domains (i.e., PETA, PA100K, MSP60K, RAPv2) and physical domains fully validated the effectiveness of our proposed adversarial attack and defense strategies for the pedestrian attribute recognition. The source code of this paper will be released on <uri>https://github.com/Event-AHU/OpenPAR</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2843-2857"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440180","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":"CLIP-ADA: CLIP-Guided Artifact-Invariant Generalizable Synthetic Image Detection","authors":"Jingyi Deng;Chong Zhang;Chenken Xu;Chenhao Lin;Zhengyu Zhao;Shuai Liu;Qian Wang;Chao Shen","doi":"10.1109/TIFS.2026.3676657","DOIUrl":"10.1109/TIFS.2026.3676657","url":null,"abstract":"The rapid advancement of generative models necessitates detection methods that generalize to synthetic images containing diverse generator and semantic artifacts. Recent research has leveraged pre-trained vision-language models, such as CLIP, to extract forensic features that distinguish real and fake images, illustrating their promising performance in synthetic image detection. However, a systematic investigation into the embedding space of CLIP to guide its principled utilization for synthetic image detection remains largely unexplored. This paper addresses this gap by first analyzing the multi-stage CLIP image embedding space to uncover its relationship with cross-artifact forensic patterns. Our findings reveal that the mid-level stages primarily encode forensic and generator artifact features, while the high-level stages primarily encode semantic artifact features. Building upon these insights, we propose the CLIP-guided Dual-level Augmentation and Forensic Distribution Adaptation (CLIP-ADA) framework to perform artifact-invariant generalizable detection. Specifically, dual-level augmentation diversifies fake embeddings and suppresses artifact encoding during training to mitigate detectors from excessively relying on artifact features. Moreover, forensic distribution adaptation reformulates synthetic image detection as identifying distributional deviations from the CLIP encoded real embeddings and thereby designing adapters to extract cross-artifact forensic features in a detection scenario-adaptive manner. Extensive evaluations on both the conventional single-generator and continual learning-based multi-generator training settings demonstrate the effectiveness of our method, both suppressing the state-of-the-art methods by over 6% of average accuracy on unseen data from more than 10 generators.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"3588-3601"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147507180","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":"Privacy-Preserving Multi-Modal Object Fusion for Connected Autonomous Vehicles: Resilience Against Malicious Third-Party Attacks","authors":"Renwan Bi;Jinbo Xiong;Xu Yang;Yuanyuan Zhang;Zhiqiang Ruan;Junfeng Tian;Xun Yi","doi":"10.1109/TIFS.2026.3677597","DOIUrl":"10.1109/TIFS.2026.3677597","url":null,"abstract":"Connected autonomous vehicles (CAVs) utilize multi-modal sensors, such as LiDAR and high-definition cameras, to collect diverse types of sensing data. Fusing object detection information from these two modalities facilitates more accurate environmental perception. In this context, lightweight secret sharing techniques are employed to protect information privacy, enabling further calculation while effectively alleviating the computational resource constraints of CAVs. Meanwhile, such techniques require an additional third-party to generate some necessary random numbers. Addressing the challenges of privacy disclosure of multi-modal object information and the reliability of random numbers, we propose a malicious third-party-resistant privacy-preserving multi-modal object fusion model, termed MPOF. First, we develop a series of secure computation protocols that do not rely on time-consuming cryptographic primitives, including secure multiplication, secure sharing conversion, and secure comparison. Leveraging the idea of sacrificial verification, we can effectively detect malicious behavior by the third-party during the random number generation process. Second, we construct a secure object bounding-box matching module based on arithmetic secret sharing (ASS), enabling similarity calculation and matching of bounding-boxes between point cloud and image modalities. Additionally, we design a secure object score fusion module that achieves fusion and updating through secure implementations of convolution, ReLU, and Maxout operations. Detailed theoretical analysis and experimental results demonstrate that, compared to secure computation protocols using homomorphic encryption for random number generation, the proposed protocols reduce computational overhead by five orders of magnitude. Furthermore, the MPOF model constructed by integrating these protocols is secure, accurate, and efficient.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"3543-3558"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147507409","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":"EPSD-HOT: Ethereum Phishing Scam Detection by Higher-Order Topology","authors":"Zhen Chen;Fang Zhou;Bo Liu;Haixia Long;Linyuan Lü","doi":"10.1109/TIFS.2026.3683272","DOIUrl":"10.1109/TIFS.2026.3683272","url":null,"abstract":"In recent years, phishing scams have become one of the most rampant criminal activities on Ethereum, causing significant financial losses to investors and disruptions to the Ethereum ecosystem. Existing phishing scam detection methods typically model Ethereum transaction records as graphs, extracting features from paired nodes based on the topological relationships. However, these methods mostly focus on low-order relational aspects, neglecting higher-order structural information in the network. In this paper, we propose a new method — Ethereum Phishing Scam Detection by Higher-Order Topology (EPSD-HOT), which improves phishing scam detection performance by mining higher-order topological features from the network. We conduct experiments on a public dataset and a crawled real-world dataset, extracting ten subgraphs with distinct network characteristics. The experimental results show that the average AUC-ROC for the ten subgraphs is 0.9970, with improvements ranging from 0.0181 to 0.1658 compared to baseline methods. This indicates that our approach is highly robust and can effectively detect phishing scams across different subgraphs while overcoming the issue of class imbalance. By incorporating higher-order structural information into node features, this work offers new insights for enhancing phishing scam detection in Ethereum.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"4235-4247"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684444","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":"Low-Cost Physical-Layer Security Design for IRS-Assisted mMIMO Systems With 1-bit DACs","authors":"Weijie Xiong;Jian Yang;Jingran Lin;Hongli Liu;Zhiling Xiao;Qiang Li","doi":"10.1109/TIFS.2026.3666900","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3666900","url":null,"abstract":"Integrating massive multiple-input multiple-output (mMIMO) systems with intelligent reflecting surfaces (IRS) presents a promising paradigm for enhancing physical-layer security (PLS) in wireless communications. However, deploying high-resolution quantizers in large-scale mMIMO arrays, along with numerous IRS elements, leads to substantial hardware complexity. To address these challenges, this paper proposes a cost-effective PLS design for IRS-assisted mMIMO systems by employing one-bit digital-to-analog converters (DACs). The focus is on jointly optimizing one-bit quantized precoding at the transmitter and constant-modulus phase shifts at the IRS to maximize the secrecy rate. This leads to a highly non-convex fractional secrecy rate maximization (SRM) problem. To efficiently solve this problem, two algorithms are proposed: 1) the WMMSE-PDD algorithm, which reformulates the SRM problem into a sequence of non-fractional programs with auxiliary variables using the weighted minimum mean-square error (WMMSE) method and solves them via the penalty dual decomposition (PDD) approach, achieving superior secrecy performance; and 2) the exact penalty product Riemannian gradient descent (EPPRGD) algorithm, which transforms the SRM problem into an unconstrained optimization over a product Riemannian manifold, eliminating auxiliary variables and enabling faster convergence with a slight trade-off in secrecy performance. Both algorithms provide analytical solutions at each iteration and are proven to converge to Karush–Kuhn–Tucker (KKT) points. Simulation results confirm the effectiveness of the proposed methods and highlight their respective advantages.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2815-2830"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148490277","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":"External Data Extraction Attacks Against Retrieval-Augmented Large Language Models","authors":"Yu He;Yifei Chen;Yiming Li;Shuo Shao;Leyi Qi;Boheng Li;Dacheng Tao;Zhan Qin","doi":"10.1109/TIFS.2026.3705326","DOIUrl":"10.1109/TIFS.2026.3705326","url":null,"abstract":"In recent years, retrieval-augmented generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs). By integrating externally retrieved information, RAG alleviates issues like outdated knowledge and, crucially, insufficient domain expertise. While effective, RAG introduces new risks of external data extraction attacks (EDEAs), where sensitive or copyrighted data in its knowledge base may be extracted verbatim by adversaries. These risks are particularly acute when RAG is used to customize specialized LLM applications with private knowledge bases. Despite initial studies exploring these risks, they often lack a formalized framework, robust attack performance, and comprehensive evaluation, leaving critical questions about real-world EDEA feasibility unanswered. In this paper, we present the first comprehensive study to formalize EDEAs against retrieval-augmented LLMs (RA-LLMs). We first formally define EDEAs and propose a unified framework decomposing their design into three components: extraction instruction, jailbreak operator, and retrieval trigger, under which prior attacks can be considered instances within our framework. Guided by this framework, we develop <sc>Secret</small>: a Scalable and EffeCtive exteRnal data Extraction aTtack. Specifically, <sc>Secret</small> incorporates (1) an adaptive optimization process using LLMs as optimizers to generate specialized jailbreak prompts for EDEAs, and (2) cluster-focused triggering, an adaptive strategy that alternates between global exploration and local exploitation to efficiently generate effective retrieval triggers. Extensive evaluations across 4 models (including 3 leading commercial LLMs) reveal that <sc>Secret</small> significantly outperforms previous attacks, and is highly effective against all 16 tested RAG instances. Notably, <sc>Secret</small> successfully extracts 35% of the data from RAG powered by Claude 3.7 Sonnet for the first time, whereas other attacks yield 0% extraction. <sc>Secret</small> is also evaluated against potential defenses. Our findings call for urgent attention to this concerning yet largely overlooked emerging threat. Code is available here.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"5864-5879"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148289098","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":"HookSteg: A Truly Lossless Steganography Based on Process Hooking for Lossy Network Channels","authors":"Mingliang Zhang;Yi Zhang;Yanmei Liu;Wentong Fan;Xiangyang Luo","doi":"10.1109/TIFS.2026.3707784","DOIUrl":"10.1109/TIFS.2026.3707784","url":null,"abstract":"Covert communication often relies on image steganography to transmit sensitive data such as commercial secrets. Any minor bit error can lead to catastrophic consequences, which imposes extremely high demands on the zero-error transmission of secret information. However, existing robust image steganographic schemes still fail to guarantee zero-error recovery of secret bits when resisting hybrid attacks such as compression and scaling on social platforms. To address this issue, we propose a lossless steganographic method called HookSteg, which bypasses lossy attacks on social platforms through process hooking, thereby achieving lossless steganography. This method first identifies nodes affected by lossy attacks through tracking whether image data has been compromised. Subsequently, a method for capturing lossy attack instructions is proposed based on syntax-semantic joint features and dynamic taint tracking techniques. Finally, process hooking is utilized to bypass lossy attack instructions, ensuring the lossless transmission of secret information. Experimental results on transmission scenarios with tens of millions of daily active users, such as Youku and Kutumb, demonstrate that HookSteg is general and requires no modifications to existing steganographic algorithms. It achieves, for the first time, lossless transmission of secret information in real-world scenarios, with an average improvement in extraction accuracy of 36.63%. Additionally, its efficiency in locating attack instructions is improved by 94.34% compared to traditional manual reverse engineering methods. Furthermore, this study resolves the trade-off among embedding capacity, security, and robustness, enabling steganography to focus on enhancing security and capacity, thereby providing a general and efficient solution for lossless steganography.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"5974-5987"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148341995","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":"Stealth in Motion: A Doppler Shift-Induced Secret Key for Securing Air–Ground Communications","authors":"Qubeijian Wang;Shaojie Bai;Wen Sun;Wei Hu;Yalin Liu;Hong-Ning Dai;Zheng Yan","doi":"10.1109/TIFS.2026.3696571","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3696571","url":null,"abstract":"The rapid evolution of uncrewed aerial vehicles (UAVs) has positioned air-ground networks as vital infrastructures for diverse applications. However, the open channels of air-ground networks remain inherently vulnerable to persistent eavesdropping threats. While physical-layer key generation (PLKG) offers a lightweight security mechanism by leveraging channel reciprocity to extract shared secrets, the inherent mobility of UAVs introduces a paradoxical tradeoff. Increased channel randomness from dynamic flight patterns enhances security through entropy amplification but simultaneously disrupts channel reciprocity, leading to key mismatch between legitimate parties. Existing PLKG schemes struggle to maintain reliability in key generation due to static channel characteristics and synchronization overhead, limiting their practical deployment in air-ground networks. To resolve this conflict, we propose a Doppler shift key generation (DSKG) scheme that systematically regulates Doppler shifts through UAV trajectory design to derive secure keys. By formulating the problem as a Markov decision process, we develop a proximal policy optimization (PPO)-clip-based reinforcement learning algorithm to dynamically control UAV speed and steering angle, ensuring robust Doppler shift reciprocity while maximizing both key entropy and generation rate. Experimental results quantify the improvements of our scheme over benchmarks in maintaining high key unpredictability and generation efficiency. Furthermore, the analysis provides valuable insights into parameter impacts, confirming the practical viability of the DSKG scheme for securing air-ground communications.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"5848-5863"},"PeriodicalIF":8.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443113","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}