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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Tianyi Huang, Mengxiao Cheng, Harry Zhang, Xiao Shen, Zhiqi Nanyang Technological University Singapore Singapore Qilu University of Technology Jinan China Shandong University Qingdao China NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios. © 2024 ACM.

关键词： Watermarking

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False Correspondence Removal via Revisiting Semantic Context with Position-Attentive Learning

False Correspondence Removal via Revisiting Semantic Context...

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IEEE International Conference on Image Processing

作者： Ruiyuan Li Zhaolin Xiao Meng Zhang Haonan Su Haiyan Jin Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

False correspondence removal remains a challenge for many feature-matching-based applications. This paper proposes a solution that revisits the local semantic context via position-attentive learning. First, a cross-divisional module is introduced to extract semantic features from image-patch pairs. Then, through a position-attentive mechanism, the parametric positions and extracted semantic features are jointly utilized to compute the probabilities of a set of putative correspondences. The proposed approach is evaluated on several indoor and outdoor challenging datasets, containing up to 70%+ false correspondences. In most cases, the solution outperforms existing algorithms in terms of matching precision and F1-score. Furthermore, an ablation study indicates that revisiting the semantic context improves precision by nearly 5%. The code is available at the link below 1 .

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A Technique for Ensured Cross Chain IBC Transactions Using TPM

A Technique for Ensured Cross Chain IBC Transactions Using T...

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Big Data and Privacy computing (BDPC), International Conference on

作者： YiChuan Wang YiLiang Yan XiaoXue Liu Wen Gao ZiYan Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures; it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer.

关键词： Data privacy Data security network security Blockchains Performance analysis Encryption security

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Code obfuscation protection strategies for program running states

Code obfuscation protection strategies for program running s...

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International Conference on networking and network Applications (NaNA)

作者： Xin Song Haonan Wang Yichuan Wang Xinhong Hei Wenjiang Ji School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

To enhance the security, complexity, and applicability of cloud node programs, effectively hinder attackers from reverse engineering, and reduce the ability of detailed analysis and vulnerability discovery of applications, in this paper, we propose a code obfuscation protection strategy based on the LLVM compiler framework oriented to the run state of the program. The approach first implements obfuscation processing on the cloud node program at the code IR layer, including control flow obfuscation, data flow obfuscation, and code virtualization obfuscation, to generate a protected executable program. After deploying the program to the cloud server, process obfuscation and memory obfuscation are performed to implement strong protection of data when the program is running. This procedure effectively guarantees the security of applications in cloud computing environments by rendering the structure and logic of the internal operation of the program inaccessible to program attackers.

关键词： Cloud computing Codes Program processors Source coding Reverse engineering Rendering (computer graphics) security

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Low Light RGB and IR Image Fusion with Selective CNN-Transformer network

Low Light RGB and IR Image Fusion with Selective CNN-Transfo...

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IEEE International Conference on Image Processing

作者： Haiyan Jin Yue Yang Haonan Su Zhaolin Xiao Bin Wang School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In low-light images, contrast and brightness are corrupted, making it difficult to accurately percept detail and edge information with the naked eye. Because of the development of multi sensor imaging, RGB-IR image fusion can enhance the imaging quality in low light conditions. However, the existing fusion algorithms have insufficient enhancement, distorted detail and low contrast, which make it difficult to generate high-quality fusion results. In this paper, we propose a Transformer-CNN image fusion method which considers global-local features fusion for low light image enhancement. The ConvGRU module is developed to alternatively select the global and local features with Transformer and CNN network. To effectively improve the network performance, a learnable weight adaptive loss function is proposed to adjust the weight of loss functions during training. Numerous experiments prove that our method can enrich fusion image information, improve image contrast and edge in low-light scenes compared to state of the art methods.

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Riemannian Complex Hermit Positive Definite Convolution network for Polarimetric SAR Image Classification

arXiv

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arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Li, Yuke Jin, Haiyan Lin, Weisi Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite (HPD) matrix endowed in Riemannian manifold rather than Euclidean space. The matrix’s real and imagery parts are with the same significance, as the imagery part represents the phase information. The matrix vectorization will destroy the geometric structure and manifold characteristics of complex covariance matrices. To learn complex HPD matrices directly, we propose a Riemannian complex HPD convolution network(HPD CNN) for PolSAR images. This method consists of a complex HPD unfolding network(HPDnet) and a CV-3DCNN enhanced network. The proposed complex HPDnet defines the HPD mapping, rectifying and the logEig layers to learn geometric features of complex matrices. In addition, a fast eigenvalue decomposition method is designed to reduce computation burden. Finally, a Riemannian-to-Euclidean enhanced network is defined to enhance contextual information for classification. Experimental results on two real PolSSAR datasets demonstrate the proposed method can achieve superior performance than the state-of-the-art methods especially in heterogeneous regions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Research on Text Classification Based on BERT-BiGRU Model 3

Research on Text Classification Based on BERT-BiGRU Model

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Yu, Qing Wang, Ziyin Jiang, Kaiwen Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

Text classification is a typical application of natural language processing. At present, the most commonly used text classification method is deep learning. Meanwhile there are many difficulties in natural language processing, such as metaphor expression, semantic diversity and grammatical specificity. To solve these problems, this paper proposes the structure of BERT-BiGRU model. First, use the BERT model instead of the traditional word2vec model to represent the word vector, the word representation is calculated according to the context information, and it can be adjusted according to the meaning of word while the context information is fused. Secondly BiGRU model is attached to the BERT model, BiGRU model can extract the text information features from both directions at the same time. Multiple sets of experiments were set up and compared with the model proposed in this paper, according to the final experimental results, using the proposed BERT-BiGRU model for text classification, the final accuracy, recall and F1 score were all above 0.9. It shows that BERT-BiGRU model has good performance in the Chinese text classification task. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Ultra-high definition video quality assessment method based on deep learning 9

Ultra-high definition video quality assessment method based ...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Wei He, Can Zhou, MingLe Zhang, HengYu Liu, WenLong Hisense Video Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

With the development of display technology, people's demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion;the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets. © 2024 IEEE.

关键词： Convolutional neural networks

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Channel Capacity Analysis for Diffusive Molecular Communication with a Partly Covered Comprehensive Reactive Receiver

Channel Capacity Analysis for Diffusive Molecular Communicat...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Zhu, Junning Liao, Xuening Wu, Zhenqiang Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an China Shaanxi Key Laboratory for Network Computing and Security Shaanxi Xi’an710048 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Molecular communication (MC) is a technology that utilizes microscopic particles such as molecules to communicate at the microscopic scale in living organisms. The nanomachine receiver as a critical part of MC has been extensively investigated. However, existing analyses of nanomachine receivers rarely consider the effects of various chemical reactions. In the few papers that considered the effect of chemical reaction on the channel, the effect of receptor coverage on the receiver surface was also not considered. To fill this gap, this paper studies the variation of channel capacity with coverage rate when the surface of the nanomachine receiver is partially covered with the receptors. This paper derived numerical results of channel capacity under the scenarios above and carried out the numerical simulations. The simulation results show that the channel capacity increases with the increase of coverage, which can guide the design of high-performance nanomachines. © 2022 IEEE.

关键词： Reaction rates

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