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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Modeling Route Representation With Mixed-Scale Hierarchical Transformer

Modeling Route Representation With Mixed-Scale Hierarchical ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanyuan Zhang Yuqi Chen Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Advanced Process Control in Manufacturing Using IoT Devices and Artificial Neural Networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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Research on intelligent penetration mechanism of netgap data based on database adaptive matching 3

Research on intelligent penetration mechanism of netgap data...

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3rd International Conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wu, Rutao Xu, Zhiwei Liu, Xuyang Liu, Wenjing Liu, Limin School of Data Science and Application Inner Mongolia University of Technology Huhehaote010000 China Inner Mongolia Autonomous Reg. Software Eng. Serv. Eng. Technology Research Center Based on Big Data Huhehaote010000 China Inner Mongolia Autonomous Region Product Quality Inspection and Research Institute Huhehaote010070 China Haihe Laboratory of Information Technology Application Innovation Tianjin300450 China Institute of Computing Technology of the Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9781510680449

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data privacy

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Devil in the Noise: Detecting Advanced Persistent Threats with Backbone Extraction

Devil in the Noise: Detecting Advanced Persistent Threats wi...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Caio M. C. Viana Carlos H. G. Ferreira Fabricio Murai Aldri Luiz Dos Santos Lourenço Alves Pereira Júnior Computer Science Division Aeronautics Institute of Technology Brazil Department of Computing and Systems Federal University of Ouro Preto Brazil Data Science & Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Federal University of Minas Gerais Brazil

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The use of host intrusion detection systems shows promising results in detecting APT campaigns due to the use of systems logs as source data to get more information about system environment. However, dealing with the increase of logs in time while tracking the execution context is a challenge for security analysts. Therefore, this work presents backbone extraction as a crucial preprocessing step, filtering out irrelevant logs. As the logs are modeled as provenance graphs, we discard spurious edges to detect residuals with distinctive node and edge distributions that indicate security threats. By applying our methodology to state-of-the-art benchmark datasets, we observed an increase in the performance of one-class classifiers by up to 62% on F1-score and 48% on recall in the Streamspot dataset and by up to 40% on F1-score and 33% on recall in the DARPA3 THEIA dataset. Moreover, our results indicate mitigation of the dependency explosion problem and underscore the ability of our methodology to improve the detection landscape by shrinking graph sizes without losing essential aspects to characterize attacks.

关键词： Computers Filtering Image edge detection Prevention and mitigation Noise Intrusion detection Benchmark testing Explosions Security

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A Review on data Quality Dimensions for Big data

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Procedia Computer science 2024年 234卷 341-348页

作者： Fakhitah Ridzuan Wan Mohd Nazmee Wan Zainon Faculty of Data Science and Computing Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kota Bharu Kelantan Institute for Artificial Intelligence & Big Data Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kota Bharu Kelantan School of Computer Sciences Universiti Sains Malaysia 11800 Penang

Big data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of ‘garbage in, garbage out’. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories.

关键词： big data data quality data quality dimension data management

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Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation 39

Deconfound Semantic Shift and Incompleteness in Incremental ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Yirui Xia, Yuhang Li, Hao Yuan, Lixin Chen, Junyang Liu, Jun Lu, Tong Wan, Shaohua Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Communication Lancaster University Lancaster United Kingdom National Key Lab for Novel Software Technology Nanjing University Nanjing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)157735897X

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Safe exploitative play with untrusted type beliefs 24

Safe exploitative play with untrusted type beliefs

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tongxin Li Tinashe Handina Shaolei Ren Adam Wierman School of Data Science The Chinese University of Hong Kong Shenzhen China Computing + Mathematical Sciences California Institute of Technology Pasadena Electrical & Computer Engineering University of California Riverside

ISBN: (纸本)9798331314385

The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors given a set of types, each specifying a possible behavior for the other agents. The idea is to plan an agent's own actions with respect to those types which it believes are most likely to maximize the payoff. However, the type beliefs are often learned from past actions and likely to be incorrect. With this perspective in mind, we consider an agent in a game with type predictions of other components, and investigate the impact of incorrect beliefs to the agent's payoff. In particular, we formally define a tradeoff between risk and opportunity by comparing the payoff obtained against the optimal payoff, which is represented by a gap caused by trusting or distrusting the learned beliefs. Our main results characterize the tradeoff by establishing upper and lower bounds on the Pareto front for both normal-form and stochastic Bayesian games, with numerical results provided.

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