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SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label

arXiv

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arXiv 2022年

作者： Hu, Shengshan Zhou, Ziqi Zhang, Yechao Zhang, Leo Yu Zheng, Yifeng He, Yuanyuan Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information Technology Deakin University VIC3216 Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security HUST Wuhan430074 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonstrated that deep neural networks (DNNs) are susceptible to adversarial examples, and exploring adversarial attack against deep hashing has attracted many research efforts. Nevertheless, backdoor attack, another famous threat to DNNs, has not been studied for deep hashing yet. Although various backdoor attacks have been proposed in the field of image classification, existing approaches failed to realize a truly imperceptive backdoor attack that enjoys invisible triggers and clean label setting simultaneously, and they also cannot meet the intrinsic demand of image retrieval backdoor. In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label. Specifically, we first propose a new conditional generative adversarial network (cGAN) pipeline to effectively generate poisoned samples. For any given benign image, it seeks to generate a natural-looking poisoned counterpart with a unique invisible trigger. In order to improve the attack effectiveness, we introduce a label-based contrastive learning network LabCLN to exploit the semantic characteristics of different labels, which are subsequently used for confusing and misleading the target model to learn the embedded trigger. We finally explore the mechanism of backdoor attacks on image retrieval in the hash space. Extensive experiments on multiple benchmark datasets verify that BadHash can generate imperceptible poisoned samples with strong attack ability and transferability over state-of-the-art deep hashing schemes. © 2022, CC BY.

关键词： Deep neural networks

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Grace: An Efficient Parallel SPARQL Query system over Large-Scale RDF data

Grace: An Efficient Parallel SPARQL Query System over Large-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing

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Text-augmented long-term relation dependency learning for knowledge graph representation

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High-Confidence computing 2025年

作者： Quntao Zhu Mengfan Li Yuanjun Gao Yao Wan Xuanhua Shi Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Knowledge graph (KG) representation learning aims to map entities and relations into a low-dimensional representation space, showing significant potential in many tasks. Existing approaches follow two categories: (1) Graph-based approaches encode KG elements into vectors using structural score functions. (2) Text-based approaches embed text descriptions of entities and relations via pre-trained language models (PLMs), further fine-tuned with triples. We argue that graph-based approaches struggle with sparse data, while text-based approaches face challenges with complex relations. To address these limitations, we propose a unified Text-Augmented Attention-based Recurrent Network, bridging the gap between graph and natural language. Specifically, we employ a graph attention network based on local influence weights to model local structural information and utilize a PLM based prompt learning to learn textual information, enhanced by a mask-reconstruction strategy based on global influence weights and textual contrastive learning for improved robustness and generalizability. Besides, to effectively model multi-hop relations, we propose a novel semantic-depth guided path extraction algorithm and integrate cross-attention layers into recurrent neural networks to facilitate learning the long-term relation dependency and offer an adaptive attention mechanism for varied-length information. Extensive experiments demonstrate that our model exhibits superiority over existing models across KG completion and question-answering tasks.

关键词： Knowledge graph representation Graph attention network Pre-trained language model Attention-based recurrent network Masked autoencoder Contrastive learning

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A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 790-799页

作者： Hao Zhang Yongdan Li Zhihan Lv Arun Kumar Sangaiah Tao Huang IEEE National Engineering Laboratory for Educational Big Data Central China Normal UniversityWuhan 430072China Lanzhou Central Sub-branch of The People’s Bank of China Lanzhou 730000China School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China School of Computing Science and Engineering Vellore Institute of Technology UniversityTamil Nadu 632014India

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is *** on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

关键词： Deep belief network(DBN) flow calculation frequent pattern intrusion detection sliding window support vector machine(SVM)

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Cross-Language Binary-Source Code Matching with Intermediate Representations

arXiv

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arXiv 2022年

作者： Gui, Yi Wan, Yao Zhang, Hongyu Huang, Huifang Sui, Yulei Xu, Guandong Shao, Zhiyuan Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The University of Newcastle Australia School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Computer Science University of Technology Sydney Australia

Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Binary codes

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Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

arXiv

引用

arXiv 2023年

作者： Wan, Yao He, Yang Bi, Zhangqian Zhang, Jianguo Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai Yu, Philip S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Simon Fraser University Vancouver Canada School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Salesforce Research United States Chongqing University China University of New South Wales Australia University of Technology Sydney Australia University of Illinois at Chicago Chicago United States

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://***). At last, we also point out several challenging and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer programming languages

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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