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作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yun-Qing Jha, Sunil Kr. School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu China Nanjing University of Information Science and Technology Nanjing China Shanxi Key Laboratory of Network and System Security Xidian University Shanxi China State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan China New Jersey Institute of Technology NewarkNJ United States

Due to the widespread use of the JPEG format, non-aligned double JPEG (NA-DJPEG) compression is very common in image tampering. Therefore, non-aligned double JPEG compression detection has attracted significant attention in digital forensics in recent years. In most of the previous detection algorithms, grayscale images are used directly, or color images are first converted into grayscale images and then processed. However, it is worth noting that most tampered images are color images. To make full use of the color information in images, a detection algorithm, which uses color images directly, is put forward in this paper. The algorithm based on refined Markov in quaternion discrete cosine transform (QDCT) domain is proposed for NA-DJPEG compression detection. Firstly, color information of a given JPEG image is extracted from blocked images to construct quaternion, and then block image QDCT coefficient matrices, including amplitude and three angles (ψ, , and θ) can be obtained. Secondly, the refined Markov features are generated from the transition probability matrix in the corresponding refinement process. Our proposed refinement method not only reduces redundant features but also makes the acquired features more efficient in detection. Therefore, the refined Markov features can not only capture the intra-block correlation between block QDCT coefficients but also improve computing efficiency in real-time. Finally, support vector machine (SVM) method is employed for NA-DJPEG compression detection. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve better detection performance with small size images (i.e., 64 × 64) outperforming state-of-the-art detection methods tested on the same dataset. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Discrete cosine transforms

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FEW-SHOT HATE SPEECH DETECTION BASED ON THE MINDSPORE FRAMEWORK

arXiv

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arXiv 2025年

作者： Qin, Zhenkai Wu, Dongze Liu, Yuxin Yang, Guifang School of Computing and Information China Network Security Research Center China Big Data and Policing Technology Laboratory Guangxi Police College Guangxi Nanning China Institute of Software Chinese Academy of Sciences 100190 China School of Information Technology Guangxi Police College 530028 China

The proliferation of hate speech on social media poses a significant threat to online communities, requiring effective detection systems. While deep learning models have shown promise, their performance often deteriorates in few-shot or low-resource settings due to reliance on large annotated corpora. To address this, we propose MS-FSLHate, a prompt-enhanced neural framework for few-shot hate speech detection implemented on the MindSpore deep learning platform. The model integrates learnable prompt embeddings, a CNN-BiLSTM backbone with attention pooling, and synonym-based adversarial data augmentation to improve generalization. Experimental results on two benchmark datasets—HateXplain and HSOL—demonstrate that our approach outperforms competitive baselines in precision, recall, and F1-score. Additionally, the framework shows high efficiency and scalability, suggesting its suitability for deployment in resource-constrained environments. These findings highlight the potential of combining prompt-based learning with adversarial augmentation for robust and adaptable hate speech detection in few-shot scenarios. © 2025, CC0.

关键词： Deep learning

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FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Wang, Fangwei Huo, Jiashuai Wang, Wei Zhang, Xi Liu, Yan Tan, Zhiyuan Wang, Changguang Hebei Normal University Key Laboratory of Network and Information Security of Hebei Province College of Computer and Cyber Security Shijiazhuang China Xidian University Hangzhou Institute of Technology Hangzhou China Edinburgh Napier University School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom

Smart Internet of Things (IoT) devices generate vast, distributed data, and their limited computational and storage capacities complicate data protection. Federated Learning (FL) enables collaborative model training across clients, enhancing performance and protecting data privacy. The Right to be Forgotten (RTBF) raises the demand for precise data removal. Federated Unlearning (FU) offers a solution for accurate data deletion in FL systems. Existing FU methods often struggle to simultaneously ensure effective data forgetting and preserve model generalization. To mitigate these challenges, an effective and robust FU framework has been proposed, which is based on the "Bad Teacher" knowledge distillation (KD), termed FedBT. First, the "Bad Teacher" KD guides the trained model to eliminate specific client contributions from the global model. Next, the frequency domain extracts the global model’s generalization components. Finally, orthogonal constraints are applied to the KD-generated gradients within the orthogonal subspace of these components, ensuring the gradients preserve the trained model’s generalization ability. FedBT eliminates the need to store historical records of parameter updates. Using orthogonal space constraints, the generalization ability of the trained model is safeguarded during unlearning. Extensive experiments on three datasets with various metrics show our method reduces accuracy by only 0.53% on MNIST, 0.26% on Fashion-MNIST, and 4.67% on CIFAR10, surpassing the best approach. Furthermore, FedBT obtains an unlearning performance that most closely approximates the results obtained from retraining from scratch. FedBT boosts IoT security by enabling the "forgetting" of certain client data, crucial for protecting user privacy and ensuring secure device interactions. © 2014 IEEE.

关键词： Sensitive data

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A Three-party Repeated Game Model for Data Privacy in Mobile Edge Crowdsensing of IoT 13

A Three-party Repeated Game Model for Data Privacy in Mobile...

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13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Zhao, Mingfeng Chen, Lei Xiong, Jinbo Tian, Youliang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9781631902710

The low request response delay of mobile edge crowdsensing (MECS) paradigm allows quick interactions among entities in practical scenarios. However, there often exist dishonest behaviors in such interactions, and the personal information leakage involved seriously threatens the privacy and security of sensing users. To tackle this problem, previously we had proposed a non-repeated three-party game model, without the consideration of multiple interactions in the actual scenario. Based on game theory, this research therefore proposes a three-party repeated game model. Specifically, we propose the corresponding social norms for different phases of sensing data. It analyzes all possible behaviors deviating from rationality, calculates the change of corresponding payoff function, and explores the influencing factors and constraints of players' honest behaviors based on the premise of maximizing interests. Finally, a significant number of simulations and numerical analyse indicate that the proposed model is feasible and effective in maximizing the benefits of game participants. Copyright © 2020 EAI.

关键词： Game theory

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Motion compensation and object detection for neuromorphic camera

Motion compensation and object detection for neuromorphic ca...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuxin Wan Rong Fei Yu Tang Xueru Bai Guo Xie Aimin Li School of Computer science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Xi’an China Guangzhou Institute Xidian University Guangzhou China School of Automation and Information Engineering Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665429825

Compared to conventional cameras, the new type of vision camera-neuromorphic cameras, which can avoid motion blur and have the advantages of high spatiotemporal resolution, high dynamic range, low latency, etc. In this paper, two motion compensation methods are introduced for operating event flow based on differential neuromorphic camera, one is motion compensation based on event counting image and time image, another is motion compensation based on image of warped events. Comprehensive experimental study is carried on two datasets, the motion compensation method based on event counting image and time image is better at generating clear motion compensation images, highlighting moving objects inconsistent with the background motion, while the motion compensation method based on image of warped events tends to generate motion compensated images of moving objects with more prominent or clearer edges. Simultaneously, two motion compensation methods are further applied to realize the object detection method based on threshold or maximum contrast. The former method can be adapted in many cases, while the latter method can well detect the translational moving target.

关键词： Image resolution Neuromorphics Image edge detection Object detection Dynamic range Cameras Motion compensation

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SRIF: Data-Free Knowledge Distillation via Stable Regulation and Input Filtering

引用

IEEE Transactions on Multimedia 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Shibiao Zhou, Jie Xu, Wenhao Gao, Longxiang Xu, Wenbo Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Data-free knowledge distillation (DFKD) enables knowledge transfer from a pre-trained teacher to a student network without accessing the real dataset. However, generator-based DFKD methods struggle to ensure that the synthetic images accurately reflect the real dataset distribution. The update of the generator network relies heavily on teacher category guidance, but varying teacher prediction accuracy across categories leads to inconsistent synthetic image quality. Such variations introduce a distribution shift between synthetic and real datasets, negatively impacting student network performance during knowledge distillation. To address this challenge, we propose the SRIF, comprising two components: Student-Driven Flexible Filtering (SDFF) and Re-weighting for Independent Regularization (RIR). SDFF filters out synthetic images affected by the category distribution shift during data generation, producing a more reliable dataset. RIR, applied during distillation, encourages the student to learn stable causal relationships through sample reweighting. Both components flexibly integrate into existing DFKD frameworks, improving performance while reducing training costs. © 1999-2012 IEEE.

关键词： Photointerpretation

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Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization

arXiv

引用

arXiv 2025年

作者： Chang, Yuanyuan Yao, Yinghua Qin, Tao Wang, Mengmeng Tsang, Ivor Dai, Guang MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University China Center for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China China

Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. However, these methods are constrained by the need for manual prompt crafting, which can be time-consuming, introduce irrelevant details, and significantly limit editing performance. In this work, we propose optimizing semantic embeddings guided by attribute classifiers to steer text-to-image models toward desired edits, without relying on text prompts or requiring any training or fine-tuning of the diffusion model. We utilize classifiers to learn precise semantic embeddings at the dataset level. The learned embeddings are theoretically justified as the optimal representation of attribute semantics, enabling disentangled and accurate edits. Experiments further demonstrate that our method achieves high levels of disentanglement and strong generalization across different domains of data. Code is available at https://***/Chang-yuanyuan/CASO. Copyright © 2025, The Authors. All rights reserved.

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Scene text image super-resolution with semantic-aware interaction

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Mingle Zhou Wenlong Liu Jin Wan Delong Han Min Li Gang Li 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) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. China

Scene text image super-resolution aims to enhance the resolution of images containing text in various scenes, which amplifies the prominence of the text and improves its recognizability. Existing methods struggle to accurately localize text regions in high-noise environments, which hampers their ability to effectively implement targeted super-resolution. To address these issues, we propose a Text image Super-resolution Semantic-aware Interaction network (TSSIN) by embedding a text region segmentation network. First, we propose to use a pre-trained text region segmentation network (TRSN) to extract text region information. This approach semantically guides our model to address the challenge of the model not performing targeted super-resolution processing of text in high-noise environments. Secondly, we propose a multi-modal semantic information interaction module (MSIIM) to mitigate the issue of insufficient global information exchange. Comprehensive experiments conducted on the TextZoom dataset demonstrate that our TSSIN significantly enhances image quality. Furthermore, it shows a clear superiority over state-of-the-art methods on TextZoom, achieving an average text recognition accuracy improvement of +1.0% over Transformer-Based Super-Resolution network (TBSRN) (49.6%, 56.2%, 60.1% vs. 48.1%, 55.9%, 58.9%), +0.83% over Parallelly Contextual Attention network (PCAN), and +1.46% over Text Prior Guided Super-Resolution (TPGSR), as evaluated by three pre-trained text recognition models. Code is available at https://***/ads2d/TSSIN .

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Numerical Calculation of Partial Differential Equation Deduction in Adaptive Total Variation Image Denoising 12

Numerical Calculation of Partial Differential Equation Deduc...

引用

12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Zhao, Minghua Wang, Qin Muniru, Abdul Nasir Ning, Jiawei Li, Peng Li, Bing School of computer science and engineering Xi'an University of Technology Xi'an Shaanxi China Shaanxi Key Laboratory of Network Computing and Security Technology Xi'an Shaanxi China

ISBN: (纸本)9781728148526

Total variation model of image denoising is easy to influence the gradient and lose the details of image. Due to these weaknesses, many adaptive total variation (ATV) models of image denoising have been proposed to eliminate the Gaussian noisy additive in the image. This paper implements the model through a numerical solution, where the gradient descent method is used to derive the partial differential equation (PDE) corresponding to the ATV model. First, based on Euler-Lagrange equation, a detailed derivation process for Partial Differential Equation is used to calculate the ATV model. Then, based on gradient descent method, a numerical calculation of the model is derived from the PDE using the Direct Difference Method. Finally, several different λ parameters are compared to produce different image denoising effects and the appropriate parameter λ value is determined. Experimental results prove that our proposed numerical calculation can effectively realize the ATV denoising model. © 2019 IEEE.

关键词： Partial differential equations

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Adaptive Fine-grained Access Control Method in Social Internet of Things

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International Journal of network security 2021年第1期23卷 42-48页

作者： Zhang, Hongbin Ma, Pengcheng Liu, Bin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal China School of Economics and Management Hebei University of Science China Technology Research Center of Big Data and Social Computing Hebei University of Science China

Social Internet of Things (SloT), as a new carrier of integration of social and Internet of Things, applies the research results of social networks from different aspects of the Internet of Things. Different types of connected intelligent objects interact socially, compared with random data access between them, access control technology is more stringent. This paper integrates social attributes into attributebased access control of Internet of Things, initializes relational attribute tags, and labels social interest attributes for different objects, then quantifes tag similarity and implements initial access control authorization, integrates social attributes into game theory to dynamically adjust access control policies, so the adaptive finegrained division of access control under the Social Internet of Things is effectively realized. The experimental results show that our method can not only effectively carry out initial authorization according to tag similarity, but also further adaptively adjust the permission policy according to social attributes, and further meet the finegrained partition requirements of access control, which is ensure the effective implementation of access control under the Social Internet of things. © 2021. All Rights Reserved.

关键词： Game theory

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