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ODTr: Transformer Integrating OCR Auxiliary Map and Image Depth information for Document Image Unwarping 27th

ODTr: Transformer Integrating OCR Auxiliary Map and Image D...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Xie, Xiangyu Zhou, Yuxuan Gao, Liangcai Wangxuan Institute of Computer Technology State Key Laboratory of Multimedia Information Processing Peking University Beijing China

ISBN: (纸本)9783031781186

In this study, we introduce a novel application of transformer for document image unwarping, leveraging depth information and Optical Character Recognition (OCR) results. Our proposed model integrates two key modules: a depth module and an OCR module, into the transformer framework tailored for document image processing. The depth module predicts the relative depth of each pixel in the document image, thereby providing crucial spatial context for unwarping. Concurrently, the OCR module identifies regions suitable for OCR, acting as preferences for the unwarping process. This hybrid approach aims to mitigate text distortion inherent in document image unwarping, consequently enhancing OCR accuracy, although it may reduce our model’s capability in structural image unwarping. Experimental results showcase our model’s effectiveness, achieving a Character Error Rate (CER) of 24.81%, marking a significant 6.2% absolute enhancement compared to the baseline method, DocTr. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Depth information Document Image Unwarping OCR Transformer

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection

Triple Path Enhanced Neural Architecture Search for Multimod...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Bo Xie, Qiujie Zhou, Jiahui Zong, Linlin School of Computer Science and Technology Dalian University of Technology China School of Computer Science Fudan University China School of Software Dalian University of Technology China

ISBN: (纸本)9798350368741

Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization ability on partial-modality contained fake news. To meet these challenges, we propose a novel and flexible triple path enhanced neural architecture search model MUSE. MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations. Experimental results show that MUSE achieves stable performance improvement over the baselines. © 2025 IEEE.

关键词： fake news detection neural architecture search

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M2PAIR: A High-Quality Acoustic Impulse Response Computation Model

M2PAIR: A High-Quality Acoustic Impulse Response Computation...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Zhiyu Zhao, Xinpei Wang, Jing Qian, Xinyuan Xie, Xiang School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Acoustic Impulse Response (AIR) provides crucial spatial information about the environment, significantly enhancing audio immersion. However, achieving high perceptual quality while computing AIR in real-time for interactive audio-video media (IAVM) presents a challenging problem. This study proposes the Mesh to Parametric AIR (M2PAIR), a method for computing AIR designed for IAVM. M2PAIR integrates neural networks with psychoacoustics. It takes the 3D scene mesh, the listener positions, and the sound source positions as inputs, utilizes perceptual parameters as intermediaries, and computes the desired high-quality AIR signal based on these parameters. Experimental results demonstrate that M2PAIR improves the perceptual quality of AIR output compared to existing methods while reducing the model complexity. Additionally, it meets the requirements of IAVM, including real-time computation, high sampling rates, and flexible duration for the output AIR. © 2025 IEEE.

关键词： Acoustic Impulse Response Auralization Deep Learning Interactive media Psychoacoustics

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A Novel Framework for Deepfake Image Detection Using Deep Learning Approach 22

A Novel Framework for Deepfake Image Detection Using Deep Le...

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22nd international Learning and technology conference, L and T 2025

作者： Jaiswal, Ambika Chamatkar, Aruna Kharat, Akash Prakash Sharma, Bhisham Dhaou, Imed Ben Chaurasia, Suhashini Awadhesh Sant Gadge Baba Amravati University P.G. Department of Computer Science Maharashtra Amravati India Kamala Nehru College Maharashtra Nagpur India Saraswati College Shegaon Department of Computer Science Shegaon India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Hekma School of Engineering Computing Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

Deepfake, fast growing field in the age of multimedia and AI, have been attracted attention in the recent years. Deep learning algorithms are used to create real digital content which are difficult to distinguish from authentic content. Deepfakes can serve multiples purposes, including educational content, academic research, social entertainment so it spread wrong information, information manipulation, damage to reputation, many frauds. Day to day deepfake crime is increasing. Detection of deepfake is big challenging issue in the digital forensics. A strong approach needs to be created from protecting against the media deepfake. The main work of this paper is to build a efficient framework for deepfake detection. The paper discusses deepfakes, detection techniques, datasets, proposed model. Novel proposed approach has been created that are based on the integration of CNN and VGG16. The deepfake dataset has been used to utilized the network architecture. Proposed model has been achieved the 95% accuracy and 94% precision score. The developed model is superior than the other state of art developed methods. © 2025 IEEE.

关键词： Dataset Deep Learning Deepfake Image processing

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Effective Minimization of Fraud Using a Hybrid Novel Approach on Social media 4

Effective Minimization of Fraud Using a Hybrid Novel Approac...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Prasanna, N.Merrin Jadala, Vijaya Chandra Raju, S.Hrushikesava Choudhury, Nabanita Adinarayna, S. Krishnaiah, Vv Jaya Rama Annamacharya University Dept. of Ece Andhra Pradesh Rajampet516216 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India Koneru Lakshmaiah Education Foundation Department of Cse Guntur522502 India Assam Down Town University Faculty of Computer Technology Assam Guwahati India Department of CSE Visakhapatnam India

ISBN: (纸本)9798331523923

In a social context, optimizing information retrieval and user engagement is paramount. The challenges identified are the large volume of data, ensuring accuracy, and performance requires evolutionary structures. A novel approach is needed that combines machine learning algorithms with evolutionary optimization techniques to automatically recommend and refine tags on known segments of social media content. This safeguards against user attention diversion and potential risks to personal information. Activities encompass automated form feedback solicitation, user-reported content negativity flagged with custom tags in red, precise identification and processing, and accuracy validation. The process initiates with an initial set of tags generated through natural language processing (NLP) techniques, subsequently honed using a genetic algorithm. This iterative process refines tag selection based on content relevance, markedly augmenting tagging precision. Concurrently, a machine learning model analyzes user behavior patterns and preferences, fine-tuning tag recommendations based on historical data. The hybrid approach surpasses conventional tagging systems, exhibiting adaptability and learning capabilities that dynamically adjust tag recommendations to mirror emerging trends and individual user interests. Additionally, integrated user feedback mechanisms fine-tune the hybrid optimization model, enabling continuous enhancement and customization. Presently, web platform owners prioritize user engagement by eliminating misleading content segments. As the volume of social media content burgeons, this approach offers a scalable and effective solution to elevate content discoverability and relevance in the digital landscape. © 2025 IEEE.

关键词： Genetic algorithms

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A Survey on Fake News Detection Using Multivariate Feature Selection and Hybrid Deep Learning Approach 1

A Survey on Fake News Detection Using Multivariate Feature S...

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1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Dongre, Aditya Kumar Kalaiarasi, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature selection combined with hybrid deep learning approaches to enhance the accuracy and robustness of fake news detection systems. The paper reviews various feature selection techniques, including chi-square, information gain, and correlation-based methods, that help in identifying the most relevant features for detecting fake news. These techniques are crucial in reducing dimensionality, improving processing time, and enhancing the performance of deep learning models. Furthermore, the study examines hybrid deep learning frameworks, which integrate multiple architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers to capture both linguistic and contextual information from news data. This survey provides a comprehensive analysis of recent advancements, highlighting key research contributions, datasets, and evaluation metrics used in fake news detection. The integration of multivariate feature selection with hybrid deep learning models offers a promising approach to addressing the complexities of fake news identification. Finally, the paper outlines the challenges and future research directions in improving model generalization, handling adversarial content, and adapting to evolving patterns of misinformation. © 2025 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Infant Cry Classification using Machine Learning and Deep Learning Techniques 1

Comparative Analysis of Infant Cry Classification using Mach...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramulu, Kothuri Parashu Jogannagari, Pranathi Reddy Indur Institute of Engineeing & Technology Department of Computer Science and Engineering Siddipet India University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798331529246

Infant Cry Classification is the process of analyzing acoustic signals from an infant's cry for extracting relevant features to determine the cause of crying, and assess the infant's health condition. The process is typically divided into four main stages: pre-processing, feature extraction, feature fusion, and classification. In the feature extraction stage, spectrograms along with frequency transforms such as the wavelet Transform, Cosine Transform, and Fourier Transforms are employed to capture important characteristics of the cry. The feature fusion stage is critical as it integrates information from various scales and contexts obtained during feature extraction, allowing for a more comprehensive representation of the cry's features. In current times, both Machine Learning (ML) and Deep Learning (DL) techniques are employed for classification. The ML techniques predominantly utilized in this process are: Multi-Class Support Vector Machines (SVM), Hyperparameter-Optimized SVM, Kernel-based SVM, Ensemble Boosting methods, Random Forest classifiers, K-Nearest Neighbors (K-NN), and Bayesian Optimization. In the case of DL architectures, models such as Style and Latent Guided Adversarial Networks (SLGAN), Multi-Layer Perceptron (MLP), Convolutional Neural Network - Sparse Convolution Net (CNN-SCNet), 1D CNN, and Dual-CNN are frequently employed to ensure high accuracy. The performance of these classification algorithms is evaluated in terms of the following metrics: sensitivity, specificity, accuracy, precision, and F-measure. © 2025 IEEE.

关键词： Cosine transforms

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An Approach for Aspect Based Sentiment Analysis Using Multivariate Feature Extraction and Machine Learning 1

An Approach for Aspect Based Sentiment Analysis Using Multiv...

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1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Patil, Priyanka Pravin Gupta, Sunil School of Computer & System Sciences JNU Jaipur India

ISBN: (纸本)9798350355611

Aspect-based sentiment analysis (ABSA) is a nuanced field within natural language processing that focuses on extracting sentiment information at a granular level, targeting specific aspects of entities within a text. This paper presents an innovative approach to ABSA by leveraging multivariate feature extraction techniques combined with advanced machine learning algorithms. Our method begins with a comprehensive pre-processing phase that includes tokenization, part-of-speech tagging, and dependency parsing to structure the textual data. We then employ multivariate feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF), word embeddings, and syntactic dependency features, to capture the contextual and semantic richness of the text. These features are fed into machine learning models, including support vector machines (SVM), random forests, and deep learning architectures like recurrent neural networks (RNN) etc. We evaluate the performance of these models using a well-defined benchmark dataset, highlighting the effectiveness of combining multivariate features with machine learning in improving the accuracy of aspect-based sentiment classification. Our experimental results demonstrate that the proposed approach significantly outperforms traditional sentiment analysis methods, providing more precise and insightful sentiment evaluations at the aspect level. This advancement holds promising applications in fields such as customer feedback analysis, social media monitoring, and market research, where understanding specific sentiment nuances is crucial. © 2025 IEEE.

关键词： Semantics

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Research on the Transfer Effect of Traditional Painting Styles Empowered by Artificial Intelligence 24

Research on the Transfer Effect of Traditional Painting Styl...

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3rd international conference on Artificial Intelligence and Intelligent information processing, AIIIP 2024

作者： Chen, Cui Wang, Hongjuan School of New Media Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9798400707308

With the vigorous development and technological advancement of artificial intelligence, style transfer technology driven by deep learning has become a hot topic in the research of artificial intelligence computer vision. This technology not only promotes the innovation pace of artificial intelligence, but also opens up a new cross disciplinary chapter by combining with traditional painting. Style transfer technology not only opens up a new path for the dissemination of traditional painting, but also promotes its deep practice of protection and innovation. This paper reviews the development of style transfer technology, and discusses the transfer effects of different style transfer models in different traditional painting styles in China through experiments, which provides a reference direction for the development of traditional painting in the field of style transfer, and also provides guidance and enlightenment for future practitioners in model selection. © 2024 Copyright held by the owner/author(s).

关键词： Traditional painting

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