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SSRN 2024年

作者： Jin, Haiyan Chen, Yujia Zuo, Fengyuan Su, Haonan Zhang, YuanLin Department of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology

Zero-reference low-light image enhancement (LLIE) techniques mainly focus on grey-scale inhomogeneities, and few approaches consider how to explicitly recover a given dark scene for achieving color and holistic illuminance enhancement. In this paper, we propose a novel Zero-Reference Color Self-Calibration framework for low-light image enhancement, namely Zero-CSC, that effectively accentuates channel-wise representations that contain fine-grained color information, achieving a natural result in a progressive manner. Furthermore, a novel Light Up (LU) module with large-kernel convolutional blocks is proposed to improve the holistic illuminance, which is implemented with a simple U-Net. The experiments are conducted on representative datasets, showing that our model consistently achieves SOTA performance on all datasets with the same architecture. © 2024, The Authors. All rights reserved.

关键词： Color

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LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks 24

LampMark: Proactive Deepfake Detection via Training-Free Lan...

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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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Robust Voice Activity Detection using Locality-Sensitive Hashing and Residual Frequency-Temporal Attention 25

Robust Voice Activity Detection using Locality-Sensitive Has...

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25th Interspeech Conferece 2024

作者： Li, Shu Zhang, Peng Li, Ye Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

For voice activity detection (VAD), recent works focus on learning the attention distribution on contextual information of speech to reduce the impact of irrelevant noise. However, contextual frames selected with specific steps may not be relevant, and these attention mechanisms can not fully discover the structure and characteristics of speech. In this paper, we explore a self-attention-inspired locality-sensitive hashing algorithm (SALSH) for dynamic and efficient contextual frame selection to enrich the frame-level features into a 2D partial spectrogram. Then, we propose a residual frequency-temporal attention model (FTAM) for VAD, consisting of an interval branch, an analogous hourglass structure with channel attention, and an attention learning mechanism for speech based on frequency-temporal attention. On the LibriSpeech and TIMIT datasets, the proposed method outperforms the others in terms of area under the curve (AUC), even under extremely low signal-to-noise ratio of -15dB. © 2024 International Speech Communication Association. All rights reserved.

关键词： Speech recognition

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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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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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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Improved Global and Local Graph Neural network for Session-based Recommendation 25

Improved Global and Local Graph Neural Network for Session-b...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Yifan Yu, Qing Wang, Zuohua Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin China China Everbright Bank Co. Ltd Beijing China

ISBN: (纸本)9781665405270

The session-based recommendation(SBR) aims to predict user actions based on anonymous sessions. SBR focuses on recent sessions to make more real-time and reliable recommendations and does not need to extract user IDs, thereby alleviating privacy issues. Recent research mainly models user preferences based on the target session while ignoring other sessions, which may contain items transitions related to and unrelated to the target session. This paper proposes a novel method, namely Improved Global and Local Graph Neural network (IGL-GNN), to models item transitions within not only the target session but also the other sessions. Specifically, we learn the session-level item representation from the local graph, learns the global-level item representation from the global graph. We propose a new method to effectively aggregate global and local representations, which not only retains more useful information but also removes useless information. In addition, we innovatively introduced Transformer, incorporated it into the model as a general deformation function, enhanced the ability to obtain complex transformations, and solved the limitations of the model in learning complex representations. We have done a lot of experiments on three benchmark datasets, and the results show that the performance of IGL-GNN is better than the most advanced methods. © 2022 IEEE.

关键词： Graph neural networks

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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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Feature Aggregation network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science 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 Shandong Computer Science Center National Supercomputer Center in Jinan Jinan 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

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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