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A Study on Deep Fake Face Detection Techniques 3

A Study on Deep Fake Face Detection Techniques

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Arya, Mudit Priyanshu Upwan Akash Goyal, Umang Chawla, Simran Chandigarh University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350375190

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era. © 2024 IEEE.

关键词： Face recognition

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Sentiment Analysis of Customer Reviews on Food Ordering Portals of Bangladesh using Natural Language Processing 6

Sentiment Analysis of Customer Reviews on Food Ordering Port...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Deb, Priom Bhuiyan, Asibur Rahman Ahmed, Farhan Hossain, Md. Rakib Mahrin, Habiba Ahmed, Md Faisal Karim, Dewan Ziaul Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505790

In recent years, the popularity of online food ordering services has surged, offering consumers a convenient way to order food from restaurants and have it delivered to their doorstep. During this period, HungryNaki and Foodpanda Bangladesh have been identified as key contributors to the growth and advancement of the online food delivery market. This study aims to anticipate the sentiments of Bangladeshi customers towards online meal ordering services, with a specific focus on Foodpanda Bangladesh and HungryNaki. We created a new dataset for this research, subjected it to preprocessing and employed six machine learning models and three deep neural network models. In the machine learning approach, the Random Forest Classifier demonstrated excellence in accuracy, precision, and recall, achieving an accuracy rate of 80.31%. On the other hand, the BERT Classifier performed effectively in the deep learning approach, reaching a peak accuracy of 89%. Despite challenges such as data ambiguity and imbalances in the dataset, our findings underscore the potential of the BERT model in sentiment analysis, offering valuable insights for future research in this field. © 2024 IEEE.

关键词： Data accuracy

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Cosmetic Product Recommendation Using Deep Learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. This is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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DERM-AI: A Convolutional Neural Network Based Approach For Skin Disease Detection

DERM-AI: A Convolutional Neural Network Based Approach For S...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Juneja, Sonam Tiwari, Parth Devi, Rajni Atwal, Shikha Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350375237

This paper shows how convolutional neural networks (CNNs) are useful for accurately and quickly diagnosing skin issues, underscoring the need of utilizing state-of-the-art machine learning approaches for accurate and fast diagnosis. A CNN architecture including convolutional and pooling layers, fully linked layers, and a softmax classifier was developed to handle the problem of diagnosing skin diseases. The CNN model was trained using stochastic gradient descent, and its hyperparameters were adjusted using grid search and random search. Future research in this area may benefit from the insights the study offers on CNN model construction and application for skin disease diagnosis. © 2024 IEEE.

关键词： Stochastic systems

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Cyberbullying Detection and Severity Classification Using Bi-LSTM 9

Cyberbullying Detection and Severity Classification Using Bi...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Devi, K. Nirmala Rajasekar, Vani Jayanthi, P. Balasubramani, Kavin Kandasamy, Kaviya Gowrisankar, Keerthana Kongu Engineering College Dept. of Computer Science Erode India

ISBN: (纸本)9798350377972

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and Natural Language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%. © 2024 IEEE.

关键词： Deep learning

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An Ensemble Machine Learning-Based Method to Determine the Presence of Heart Ailment Using Pooled Dataset

An Ensemble Machine Learning-Based Method to Determine the P...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Saad, Mirza Nadim Jahan, Esrat Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Heart disease remains the leading cause of mortality globally, emphasizing the critical need for better diagnostics. This study used a variety of machine learning methods to forecast cardiac disease, using SVM, Random Forest, Decision Tree, KNN, and LGBM. The evaluation measures include accuracy, ROC, precision, recall, and F1 score. Data pretreatment procedures such as cleansing, transformation, integration, and reduction were used to guarantee reliable analysis. The model's performance was further enhanced by hyperparameter tuning. SVM had the lowest performance of the models examined, with 57.23% accuracy, 86% precision, 89% recall, and an F1 score of 87%. The study used three different datasets, combining their strengths to improve model discrimination, decrease overfitting, and increase overall accuracy. We developed multiple models from the combination of RF, DT and LGBM. We found accuracy of up to 99.54% by merging LGBM and Random Forest. These findings illustrate the efficacy of the ensemble approaches and data integration in improving heart disease prediction skills using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Name Entity Recognition and Text Classification for Talent Mapping 2

Leveraging Name Entity Recognition and Text Classification f...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Charan, Annepu Sai Moirangthem, Dennis Singh Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen resumes, presents operational issues in the quickly changing software sector. Although this technique matches resumes with job descriptions effectively, it requires separate scans for every job posting, which could result in errors and inefficiencies. This initiative is motivated by the need to expedite the hiring process for tech companies and potential employees. The suggested approach presents a cutting-edge tool to improve and expedite job matching and resume analysis. The work attempts to improve the software industry's hiring process efficiency by overcoming present system restrictions. This study presents a novel solution - a text classification model intended for resume analysis and shortlisting to address the operational difficulties with the modern resume screening process. Even though the Application Tracking System (ATS) is currently used, it requires separate scans for every job posting, which could lead to inefficiencies. We propose dual-model architecture which includes our custom Name Entity Recognition (NER) Model which is trained on annotated work experience data and a Multi-Layer Perceptron (MLP) model for multi label classification, aiming to streamline the resume screening process. This dual-model approach enhances accuracy and efficiency in resume classification, benefiting both aspirants and tech companies. This research is a pivotal step in optimizing recruitment in the dynamic software industry. The complete work can be found on GitHub1 © 2024 IEEE.

关键词： Mapping

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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