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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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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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Skin Cancer Classifier: Performance Enhancement Using Deep Learning Models

Skin Cancer Classifier: Performance Enhancement Using Deep L...

引用

International conference on Signal processing and communication (ICSC)

作者： Swati Mishra Megha Agarwal Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Skin cancer ranks among the most prevalent human malignancies, primarily diagnosed through visual methods, commencing with clinical screening, followed by dermoscopic analysis. The intricate alterations in the visual appearance of skin lesions pose a considerable challenge for automated classification through images. Deep learning techniques have been harnessed to support dermatologists in enhancing diagnostic accuracy. To address this, deep convolutional neural networks (CNNs) are employed to achieve highly discriminating and potentially generalizable results for image classification problems. This research introduces a prediction model designed to classify skin lesions into seven categories using the HAM10000 skin cancer dataset. The MobileNetV2, Resnet50, and DenseNet121 models were used in this study. The MobileNetV2 outperforms with 99.72 % accuracy and achieves 0.88, 0.89, and 0.88 values of precision, recall, and F1 score.

关键词： Deep learning visualization Analytical models Accuracy Computational modeling Skin Real-time systems Lesions Skin cancer Residual neural networks

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Enhanced Image Steganography Using Customized Differences between the Neighboring Pixels

Enhanced Image Steganography Using Customized Differences be...

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International conference on Signal processing and communication (ICSC)

作者： Maulana Alirridlo Adifa Widyadhani Chanda D'Layla Ntivuguruzwa Jean De La Croix Tohari Ahmad Departement of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia University of Rwanda Kigali Rwanda

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In the digital era, safeguarding sensitive information from unauthorized access is paramount. Image steganography, which inconspicuously embeds data into visual media, has emerged as a promising solution. However, balancing data embedding capacity with image quality remains a challenge. This study introduces a novel steganographic method that optimizes visual fidelity and payload capacity by utilizing customized differences between neighboring pixels within grouped blocks. Experimental results demonstrate the effectiveness of the proposed method, achieving a Peak Signal-to-Noise Ratio (PSNR) of up to 48.99 dB and a Mean Squared Error (MSE) as low as 1.20 on complex images like “Baboon.” Additionally, Structural Similarity Index Measure (SSIM) values consistently reach a perfect 1.00 across several test cases. The method also achieves a significant payload increase, embedding up to 119,496 bits in the “Resolution Chart” image. By leveraging the precise control over pixel differences, this study introduces a new paradigm in steganography, addressing the trade-off between capacity and quality while ensuring imperceptibility and high efficiency.

关键词： Image quality Steganography visualization PSNR Measurement uncertainty information security Signal processing Media Indexes Payloads

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Efficient Allocation of Image Recognition and LLM Tasks on Multi-GPU System 15th

Efficient Allocation of Image Recognition and LLM Tasks on...

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15th International conference on Parallel processing and Applied Mathematics, PPAM 2024

作者： Lawenda, Marcin Samborski, Krzesimir Khloponin, Kyrylo Szustak, Lukasz Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznań61-139 Poland Czestochowa University of Technology Dąbrowskiego 69 Częstochowa42-201 Poland

ISBN: (纸本)9783031857027

This work is concerned with the evaluation of the performance of parallelization of learning and tuning processes for image classification and large language models. For machine learning model in image recognition, various parallelization methods are developed based on different hardware and software scenarios: simple data parallelism, distributed data parallelism, and distributed processing. A detailed description of presented strategies is given, highlighting the challenges and benefits of their application. Furthermore, the impact of different dataset types on the tuning process of large language models is investigated. Experiments show to what extent the task type affects the iteration time in a multi-GPU environment, offering valuable insights into the optimal data utilization strategies to improve model performance. Furthermore, this study leverages the built-in parallelization mechanisms of PyTorch that can facilitate these tasks. Furthermore, performance profiling is incorporated into the study to thoroughly evaluate the impact of memory and communication operations during the training/tuning procedure. Test scenarios are developed and tested with numerous benchmarks on the NVIDIA H100 architecture showing efficiency through selected metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Dossier Classification to Support Workflow Management Optimization 4th

Dossier Classification to Support Workflow Management Optim...

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Fioretto, Simona Masciari, Elio Napolitano, Enea Vincenzo Department of Electrical and Information Technology Engineering University of Naples Federico II Naples Italy Arcavacata Italy

ISBN: (纸本)9783031812460

One of the main challenges in optimizing services in various fields, including the legal sector, is the lack of an automatic system that can efficiently assign tasks based on predefined rules. In many cases, documents are still processed manually by qualified staff, which can be time-consuming and prone to errors. To address this issue, there is a need to develop a system that can automate the classification process, thereby simplifying the allocation of tasks to the most competent professionals in a specific field. This can be achieved by using Natural Language processing (NLP) algorithms. In our Case Study the problem is to classify legal documents and suggest the most suitable judiciary branch for each dossier. By implementing such a system, it is possible to reduce processing times and enhance the overall efficiency of judicial offices. The proposed method can be supported by using a legal distributed system, which can facilitate seamless communication and collaboration among different actors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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A Critical Assessment of visual Sound Source Localization Models Including Negative Audio

A Critical Assessment of Visual Sound Source Localization Mo...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xavier Juanola Gloria Haro Magdalena Fuentes Universitat Pompeu Fabra Barcelona Spain MARL-IDM New York University New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The task of visual Sound Source Localization (VSSL) involves identifying the location of sound sources in visual scenes, integrating audio-visual data for enhanced scene understanding. Despite advancements in state-of-the-art (SOTA) models, we observe three critical flaws: i) The evaluation of the models is mainly focused in sounds produced by objects that are visible in the image, ii) The evaluation often assumes a prior knowledge of the size of the sounding object, and iii) No universal threshold for localization in real-world scenarios is established, as previous approaches only consider positive examples without accounting for both positive and negative cases. In this paper, we introduce extended test sets and new metrics designed to complete the current standard evaluation of VSSL models by testing them in scenarios where none of the objects in the image corresponds to the audio input, i.e. a negative audio. We consider three types of negative audio: silence, noise and offscreen. Our analysis reveals that numerous SOTA models fail to appropriately adjust their predictions based on audio input, suggesting that these models may not be leveraging audio information as intended. Additionally, we provide a comprehensive analysis of the range of maximum values in the estimated audio-visual similarity maps, in both positive and negative audio cases, and show that most of the models are not discriminative enough, making them unfit to choose a universal threshold appropriate to perform sound localization without any a priori information of the sounding object, that is, object size and visibility.

关键词： Location awareness Measurement visualization Analytical models Noise Predictive models Speech processing Standards Testing Videos

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Vision Transformers for Mpox Detection

Vision Transformers for Mpox Detection

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International conference on Artificial Intelligence in information and communication (ICAiiC)

作者： Gelan Ayana So-Yun Park Se-Woon Choe Medical IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Monkeypox (Mpox) has emerged as a significant public health concern, with increasing cases reported worldwide. Accurate and timely diagnosis is crucial for effective treatment and containment. Traditional diagnostic methods, such as polymerase chain reaction, though reliable, are time-consuming and require specialized equipment. Image-based detection methods have gained attention as a complementary diagnostic tool. Conventional image-based methods rely heavily on convolutional neural networks (CNNs); however, vision transformers (ViTs) have emerged as a novel deep learning approach with potential benefits in medical imaging. CNN shave been the cornerstone of medical image analysis, but they come with limitations, particularly in handling complex and diverse visual data, whereas ViTs, originally designed for natural language processing, have shown remarkable potential in visual tasks due to their ability to capture long-range dependencies in images. This paper explores the use of ViTs, a novel deep learning architecture, in detecting Mpox. The study compares ViTs with existing CNN-based methods, evaluates their performance in detecting Mpox, and discusses the implications of their use in clinical settings. Our findings suggest that ViTs offer superior accuracy, making them a valuable tool in disease detection.

关键词： Computer vision visualization Accuracy Materials reliability Transformers Natural language processing Convolutional neural networks Polymers Public healthcare Medical diagnostic imaging

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Fabrication and Characterization Glass/AZO/Ag Ultraviolet Photodetector

Fabrication and Characterization Glass/AZO/Ag Ultraviolet Ph...

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International conference on Signal processing and communication (ICSC)

作者： Rishibrind Kumar Upadhyay Shivani Sharma Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In this article, we demonstrate the succinct report on Al-doped Zinc Oxide (AZO) and pristine Zinc Oxide (ZnO) based ultraviolet photodetector device fabrication on the glass substrate. Pristine Zinc oxide (ZnO) and Al-doped ZnO (AZO) nanoparticles were prepared using a simple chemical sol-gel route. The prepared ZnO and Al-doped ZnO (AZO) nanoparticles have been characterized using XRD, and high-resolution SEM for phase formation, and surface morphology respectively. XRD graph confirmed the formation of the wurtzite hexagonal structure of ZnO. The device performance was measured using a semiconductor parametric analyzer with a UV light source for -2V to 2V.

关键词： Fabrication Nanoparticles Performance evaluation X-ray scattering ii-VI semiconductor materials Surface morphology Doping Zinc oxide Photodetectors Chemicals

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17th Asian conference on Intelligent information and Database Systems, ACiiDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent information and Database Systems, ACiiDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on Intelligent information and Database Systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency Image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute information;continuous Recognition of Mouth Patterns in Japanese Sign Language for visual communication;Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning Systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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17th Asian conference on Intelligent information and Database Systems, ACiiDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent information and Database Systems, ACiiDS 2025

ISBN: (纸本)9789819660070

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