標(biāo)題: Titlebook: Artificial Intelligence Security and Privacy; First International Jaideep Vaidya,Moncef Gabbouj,Jin Li Conference proceedings 2024 The Edi [打印本頁] 作者: Buren 時(shí)間: 2025-3-21 16:54
書目名稱Artificial Intelligence Security and Privacy影響因子(影響力)
書目名稱Artificial Intelligence Security and Privacy影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence Security and Privacy網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence Security and Privacy網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence Security and Privacy被引頻次
書目名稱Artificial Intelligence Security and Privacy被引頻次學(xué)科排名
書目名稱Artificial Intelligence Security and Privacy年度引用
書目名稱Artificial Intelligence Security and Privacy年度引用學(xué)科排名
書目名稱Artificial Intelligence Security and Privacy讀者反饋
書目名稱Artificial Intelligence Security and Privacy讀者反饋學(xué)科排名
作者: 提名的名單 時(shí)間: 2025-3-21 23:47 作者: fibula 時(shí)間: 2025-3-22 00:38 作者: 忙碌 時(shí)間: 2025-3-22 04:35 作者: Free-Radical 時(shí)間: 2025-3-22 09:51 作者: 陳舊 時(shí)間: 2025-3-22 13:02
Active Defense Against Image Steganography, added with the original cover image as the enhanced cover image. On the side of the steganographer, a steganographic network is applied to perform message embedding and extraction on the enhanced cover image. The key of ADPI is that the perturbation map is optimized with the goal of reducing the ac作者: 自由職業(yè)者 時(shí)間: 2025-3-22 18:59 作者: ANN 時(shí)間: 2025-3-22 22:38 作者: 打折 時(shí)間: 2025-3-23 04:02 作者: 約會(huì) 時(shí)間: 2025-3-23 07:03 作者: 輕推 時(shí)間: 2025-3-23 12:16
Integrating Research and Pedagogyithin cover image and generate a stego image. From the side of the receiver, according to a transmitted private key, RevealNet can be applied to reveal the corresponding secret image from the stego image. Experimental results show that DEMIHAK outperforms existing method from the perspective of visu作者: Deduct 時(shí)間: 2025-3-23 15:45 作者: 吃掉 時(shí)間: 2025-3-23 18:04
https://doi.org/10.1007/978-3-658-21949-9ly avoids interference and improves the probability of success. Based on the dynamic and unpredictable needs of Ad Hoc networks, we try to use DQN strategy to train the network’s agents without expert knowledge. Furthermore, we demonstrate the performance of the proposed algorithm by comparing it wi作者: Injunction 時(shí)間: 2025-3-24 01:29
Wirtschaftsethik in der globalisierten Weltcan defend against six state-of-the-art backdoor attacks. In comparison to the other four defense methods, DFaP demonstrates superior performance with an average reduction in attack success rate of 98.01%.作者: FISC 時(shí)間: 2025-3-24 05:02 作者: 向外才掩飾 時(shí)間: 2025-3-24 07:39
Misha Pavel,Helen A. Cunningham added with the original cover image as the enhanced cover image. On the side of the steganographer, a steganographic network is applied to perform message embedding and extraction on the enhanced cover image. The key of ADPI is that the perturbation map is optimized with the goal of reducing the ac作者: Junction 時(shí)間: 2025-3-24 13:36
Andries S. Brandsma,Ronald H. Ketellappermoving target strategies, intrusion tolerance strategies, and mimic defense strategies. Secondly, based on the mimic defense strategy, we provide a detailed introduction to mimic routers and mimic server technologies, which simulate normal network traffic and service behavior to enhance system secur作者: Irritate 時(shí)間: 2025-3-24 16:24
The analysis of geographical maps in chip inductor surface defect detection enhances efficiency, addresses the issue of lengthy DETR model training and poor small object detection performance, achieves classification and localization of chip inductor surface defects, and validates the feasibility of the detection method.作者: 植物群 時(shí)間: 2025-3-24 20:40 作者: 翻動(dòng) 時(shí)間: 2025-3-24 23:09 作者: fleeting 時(shí)間: 2025-3-25 05:02
,Towards Heterogeneous Federated Learning: Analysis, Solutions, and?Future Directions,作者: 頂點(diǎn) 時(shí)間: 2025-3-25 11:00 作者: 不自然 時(shí)間: 2025-3-25 13:37 作者: violate 時(shí)間: 2025-3-25 17:20
https://doi.org/10.1057/978-1-137-45344-0h different access policy to the ciphertext of the same plaintext should retrieve this ciphertext normally, we propose a novel ABE scheme supporting fine-grained authorized secure deduplication. Compared with the related works, we consider the dynamic policy update which adapts to the real-world environment more.作者: 按等級(jí) 時(shí)間: 2025-3-25 23:41
The Synthesis of Vision and Actionad of subgraph matching without leakage of the sensitive information of graphs. This paper presents a survey of recent methods for privacy-preserving subgraph matching. Finally, this paper provides valuable insights and possible future directions.作者: 生存環(huán)境 時(shí)間: 2025-3-26 02:47 作者: 終點(diǎn) 時(shí)間: 2025-3-26 07:12
,A Survey of?Privacy Preserving Subgraph Matching Methods,ad of subgraph matching without leakage of the sensitive information of graphs. This paper presents a survey of recent methods for privacy-preserving subgraph matching. Finally, this paper provides valuable insights and possible future directions.作者: Neuropeptides 時(shí)間: 2025-3-26 11:54 作者: 推崇 時(shí)間: 2025-3-26 16:25 作者: 出沒 時(shí)間: 2025-3-26 18:31 作者: myelography 時(shí)間: 2025-3-27 00:33 作者: conquer 時(shí)間: 2025-3-27 04:32
,Protecting Bilateral Privacy in?Machine Learning-as-a-Service: A Differential Privacy Based Defenseo query requests and model responses, both the client and server sides in MLaaS are privacy-protected. Experimental results also demonstrate the effectiveness of the proposed solution in ensuring accuracy and providing privacy protection for both the clients and servers in MLaaS.作者: HALO 時(shí)間: 2025-3-27 09:13
,An Embedded Cost Learning Framework Based on?Cumulative Gradient Rewards,the steganography problem. In this framework, the reward function assigns distortion values to each pixel of the image and relates the security performance of steganography. Based on the conducted experiments, an enhanced steganographic embedding scheme can ultimately be achieved.作者: 行乞 時(shí)間: 2025-3-27 11:56 作者: 結(jié)果 時(shí)間: 2025-3-27 15:36
https://doi.org/10.1007/978-3-031-02260-9ues to generate the transformed dataset of test set and shapelet; finally, combine with KNN classifier for network traffic anomaly detection. In this paper, multi-classification experiments are conducted on one available dataset, NSL-KDD with 99.18. accuracy, and the experimental results are analyzed for model solvability.作者: Vasodilation 時(shí)間: 2025-3-27 18:26 作者: Receive 時(shí)間: 2025-3-28 01:00 作者: 舞蹈編排 時(shí)間: 2025-3-28 04:39 作者: 獨(dú)輪車 時(shí)間: 2025-3-28 10:06 作者: Gleason-score 時(shí)間: 2025-3-28 14:20
,Converging Blockchain and?Deep Learning in?UAV Network Defense Strategy: Ensuring Data Security Durce UAV safety during operations and provide robust protection against cyber threats. A series of experimental tests were conducted, simulating various UAV network attack scenarios. The results of these experiments unequivocally demonstrate the feasibility and effectiveness of the blockchain-driven UAV network service architecture.作者: Legion 時(shí)間: 2025-3-28 16:23 作者: MAIM 時(shí)間: 2025-3-28 21:41 作者: ADJ 時(shí)間: 2025-3-28 23:12 作者: 極肥胖 時(shí)間: 2025-3-29 06:41 作者: Gene408 時(shí)間: 2025-3-29 08:24 作者: indubitable 時(shí)間: 2025-3-29 12:59
,A Network Traffic Anomaly Detection Method Based on?Shapelet and?KNN,ogies such as port masquerading and traffic encryption, traditional traffic anomaly detection methods face many difficulties in dealing with large-scale, high-dimensional, and diverse network traffic data, such as traffic features needing to be more abstract and the model being uninterpretable. In t作者: FAST 時(shí)間: 2025-3-29 16:05 作者: 不利 時(shí)間: 2025-3-29 20:32
,DFaP: Data Filtering and?Purification Against Backdoor Attacks,ire data from unsecured external sources through automated methods or outsourcing. Therefore, severe backdoor attacks occur during the training data collection phase of the DNNs pipeline, where adversaries can stealthily control DNNs to make expected or unintended outputs by contaminating the traini作者: 動(dòng)作謎 時(shí)間: 2025-3-30 03:29 作者: Culmination 時(shí)間: 2025-3-30 06:07 作者: heterodox 時(shí)間: 2025-3-30 08:26 作者: NAV 時(shí)間: 2025-3-30 14:47 作者: 輕率的你 時(shí)間: 2025-3-30 18:14 作者: 周年紀(jì)念日 時(shí)間: 2025-3-30 23:25 作者: Expressly 時(shí)間: 2025-3-31 03:49 作者: 柏樹 時(shí)間: 2025-3-31 05:18 作者: Reclaim 時(shí)間: 2025-3-31 10:28 作者: Genome 時(shí)間: 2025-3-31 14:00
FedCMK: An Efficient Privacy-Preserving Federated Learning Framework,arning updates the global model by updating the gradient, an attacker may still infer the model update through backward inference, which may lead to privacy leakage problems. In order to enhance the security of federated learning, we propose a solution to this challenge by presenting a multi-key Che作者: circumvent 時(shí)間: 2025-3-31 17:52
,An Embedded Cost Learning Framework Based on?Cumulative Gradient Rewards,orks. The GAN has the potential to effectively generate artificial samples that closely resemble the actual sample distribution. The field of steganography utilizing the Generative Adversarial Network (GAN) structure has witnessed a wealth of research with highly successful outcomes. This paper prop作者: 羊欄 時(shí)間: 2025-4-1 01:04
https://doi.org/10.1007/978-981-99-9785-5Machine learning; Adversarial machine learning; Malware detection and analysis; Privacy-preserving data作者: 浮夸 時(shí)間: 2025-4-1 04:05
978-981-99-9784-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: insecticide 時(shí)間: 2025-4-1 09:59
Artificial Intelligence Security and Privacy978-981-99-9785-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: acquisition 時(shí)間: 2025-4-1 13:09
