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Unleashing Password Potential: VAEs vs GANs in a Password Guessing Duel

Unleashing Password Potential: VAEs vs GANs in a Password Gu...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Salve, Siddhant Punn, Narinder Singh Dewangan, Deepak Kumar Agarwal, Sonali ABV-IIITM Dept. of Computer Science and Engg. Gwalior India Iiit Allahabad Dept. of Information Technology Prayagraj India

ISBN: (纸本)9798350376876

Insecure passwords remain a persistent vulnerability in cybersecurity. This study investigates the potential of employing Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) as tools for generating synthetic passwords, with the aim of enhancing existing password cracking methodologies. These models, trained on a dataset of leaked RockYou passwords, are evaluated based on their capacity to produce passwords that replicate the complexity and diversity observed in real-world passwords. Through a thorough assessment of the quality and characteristics of these synthetic passwords, this research reveals the strengths and weaknesses of each model. Additionally, the study examines the effectiveness of utilizing synthetic passwords in password cracking endeavors, comparing their performance to established methods and actual breached passwords. In conclusion, this research seeks to deepen our comprehension of password creation patterns and fortify our ability to counter the risks associated with weak passwords. By delving into the potential of VAEs and GANs, we contribute to the ongoing pursuit of robust cybersecurity measures and the safeguarding of sensitive information. © 2024 IEEE.

关键词： Generative adversarial networks

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Expressing Liminality in Data within the Context of Deep Learning 5

Expressing Liminality in Data within the Context of Deep Lea...

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5th International Conference on Computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2024

作者： Mehmeti, Fatjon Elmazi, Donald Kulla, Elis IT University Metropolitan Tirana Dept. of Computer Science Tirana Albania Fukuoka Institute of Technology Dept. of Information Management Fukuoka Japan

ISBN: (纸本)9798331534530

In this paper we describe the concept of Liminality and how it manifests in Feature Spaces of Data during processes of Deep Learning, with particular focus on data during transition, transformation and other states where Liminality manifests in Data Feature Spaces and between various dimensions, Hyper-planes and Decision Boundaries. To go about this, we approached a decision boundary and gave it a threshold, which would behave as an event horizon, that way data can be measured to be in that threshold or out of it. In our work, we discovered that deploying solutions, by consulting a metric of liminality leads to an ability to understand whether a model being used is appropriate, or not, by seeing how its rearrangement, affects the data's proximity to the threshold. In summary, this paper addresses the problem of lack of dept. in the study of the evaluation of a Deep Learning algorithm, through the use of a liminality metric which measures which data is subject to certain techniques like feature selection, and pruning, or models that select progressively narrower sections of data from a given feature space. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Feature engineering and Sampling Strategies for Enhanced Diabetic Retinopathy Classification 3

Hybrid Feature Engineering and Sampling Strategies for Enhan...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Kumar Khan, Ajoy Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Ophthalmologist Aizawl India

ISBN: (纸本)9798331523893

This study presents a machine learning approach for Diabetic Retinopathy (DR) classification, integrating advanced preprocessing, feature extraction, and adaptive sampling. Preprocessing techniques, including CLAHE, gamma correction, and noise reduction, standardize image quality while preserving anatomical details. Class imbalance is addressed with ADASYN, generating synthetic samples in sparse regions for balanced class representation. Feature extraction focuses on clinical markers like vascular density, microaneurysms, exudates, and texture descriptors such as GLCM and LBP. Recursive Feature Elimination (RFE) refines features for better interpretability and reduced computational load. This binary classification framework achieves 92.91% accuracy on test data and a cross-validated ROC AUC of 0.994, demonstrating the effectiveness of hybrid feature engineering and intelligent sampling in DR detection. © 2025 IEEE.

关键词： Photointerpretation

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An Efficient Detection of Floating Marine Debris using Deep Learning Techniques

An Efficient Detection of Floating Marine Debris using Deep ...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Pandey, Prachi Guru Prasad, M.S. Mayank, Kumar Gowda, Mahima M Prashanth Kumar, S.P. Karthik, D.U. Dept. of Computer Science and Engg. Dehradun India REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science and Engg. Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350344509

Growing concerns about the environmental consequences of floating debris in aquatic ecosystems have underscored the need for the development of efficient and automated methods for debris classification and monitoring. This research paper presents a comprehensive investigation into the application of neural networks, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM architectures, for this purpose. Our study entails the creation of a diverse dataset encompassing various debris types and environmental conditions, ensuring its real-world relevance and generalizability. Thorough exploration is conducted of the three neural network models to evaluate their effectiveness in classifying floating debris from images captured in aquatic environments. CNNs are chosen for their established image recognition capabilities, while LSTMs are incorporated to capture sequential information and potential temporal dependencies within debris trajectories. The outcomes of our study unveil the strengths and limitations of CNN, CNN-LSTM, and LSTM architectures in the context of floating debris classification. Proposed work provides insights into the suitability of each model in various real-world scenarios, including riverine systems, oceans, and coastal regions. Furthermore, we discuss the implications of our findings for environmental monitoring, debris removal strategies, and policy development. Throughout our research, each model's performance is assessed in terms of accuracy, precision, recall, and F1-score, considering the unique challenges posed by the inherently noisy and dynamic nature of aquatic environments. This research contributes to the evolving body of knowledge regarding the application of neural networks in environmental monitoring, with a particular focus on the critical domain of floating debris classification. Our findings are expected to guide the development of automated systems that can aid in mitigating the environmental i

关键词： Debris

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Passing Practice Support for Soccer Players Using First-Person Videos 13

Passing Practice Support for Soccer Players Using First-Pers...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Umezu, Nobuyuki Imada, Yuya Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798350373332

We propose a novel visualization system to enhance soccer passing practice by providing potential pass courses and their scores. The proposed system is based on a first-person video footage captured during soccer play and extracts frames for pass-course suggestions using the rate of ground-surface pixels in the frame and dept. estimation. Possible pass courses were scored according to their direction and distance to the closest player. To validate the proposed method's effectiveness, we conducted a series of experiments involving 17 participants, comprising 12 museum visitors and five university students. The estimated score of visualized pass courses using the proposed method was generally successful, whereas some pass courses received lower scores from participants owing to limitations in the system's ability to accurately detect the color of players' uniforms, which affected the overall evaluation of certain passing options. © 2024 IEEE.

关键词： Visualization

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Interactive Museum Kiosk for Visualizng Visitor Activities 13

Interactive Museum Kiosk for Visualizng Visitor Activities

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Umezu, Nobuyuki Nishida, Saku Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798350355079

We propose an interactive information kiosk for tracking and visualizing visitor activities in museums. Each visitor is assigned a sheet of paper with one's own QR code and recocnized by presenting the code before interacting with our kiosks and other exhibits. The proposed kiosk has a 32 inch LCD built into a wooden stand and a inexpensive two-dimensional LiDAR sensor mounted at the top to detect visitor interactions of hand gestures towards the display surface. We implemented a four-choice quiz with 40 questions, an personal review of visitor activity, and a museum floor guide which can be scrolled using hand gestures. A server system has been also developed to collect information from kiosks and summarize it. A series of experiments was conducted in a local science museum for two days to evaluate the proposed system to track and visualize visitor activities. Our future work includes improving the system with various sensors and larger-scale experiments with more kiosks with multiple types of content. © 2024 IEEE.

关键词： Liquid crystal displays

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Sign Language Detection Using Action Recognition LSTM Deep Learning Model 2

Sign Language Detection Using Action Recognition LSTM Deep L...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Snehalatha, N. Mohana Kumar, S. Kavitha, H. Nisha, S. Gowda, Puttabore Krishna Prasad, Sj Jss Academy of Technical Education Dept. of Computer Science and Engineering Bangalore India Bangalore India Siddaganga Institute of Technology Dept. of Information Science Tumukur India Bangalore India Ramaiah Institute of Technology Dept. of Me Bangalore India Visvesvaraya College of Engineering Dept. of Ec University Bangalore India

ISBN: (纸本)9798350372892

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities © 2024 IEEE.

关键词： Deep neural networks

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A Novel Approach to Predict the Satellite Image Using CNN-ResNet Model Through Flask Application 9

A Novel Approach to Predict the Satellite Image Using CNN-Re...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Madhuri, Ch. Bindu Prathima, Ch. Jyoshna, R. Varsha, Varikunta Sri Khan, Phatan Ayub Babu, Sammeta Mahesh JNTUGV-College of Engineering Department of Information Technology Vizianagaram India Dept. of Ds A.Rangampet Tirupati India Sree Vidyanikethan Engineering College Dept. of Information Technology A.Rangampet Tirupati India

ISBN: (纸本)9798350365092

In this work, we provide a hybrid Convolutional Neural Network (CNN) architecture coupled with Residual Network (ResNet) components, offering a robust method for satellite image prediction. By addressing issues with feature extraction and information degradation in deep networks, this novel approach seeks to improve the precision and effectiveness of predictions made from satellite images. By fusing residual connections with the advantages of standard CNNs, the hybrid architecture enhances gradient flow during training and facilitates efficient feature learning. Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model's performance once it has undergone thorough testing on a wide range of datasets that cover different geographic regions. The hybrid CNN-ResNet architecture is positioned as a potent tool for precise land cover categorization, environmental monitoring, and geospatial analytics in a variety of remote sensing applications, thanks to comparative studies conducted against baseline CNN models. © 2024 IEEE.

关键词： Remote sensing

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AI-Powered DeepFake Defense 3

AI-Powered DeepFake Defense

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

作者： Chaitra, M. Kushal, B. Likhitha, H. Priyanka, H.R. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Deepfake technology, known for its ability to produce convincingly face-swapped videos, presents significant risks to privacy, security, and public trust. Misuse of this technology for spreading misinformation, defamation, and influencing politics is on the rise, creating a pressing need for more effective detection mechanisms. As deepfake generation methods continue to advance, conventional detection techniques are increasingly unable to keep up, often falling short in accuracy and adaptability. This project aims to create an AI-driven detection system specialized in identifying face-swap deepfakes. The system focuses on subtle irregularities in facial details, movements, and the synchronization between audio and visuals. Employing neural network models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Capsule Networks, it pinpoints anomalies like unnatural expressions, atypical skin textures, and lighting inconsistencies. The system achieves high reliability with metrics including accuracy (96%), precision (95%), recall (94%), and F1 score (95%), showing marked improvement compared to traditional techniques. It outperforms existing methods such as XceptionNet (92%) on the Celeb-DF dataset, underscoring its superior generalizability and robustness. This scalable solution has broad applications in media, law enforcement, and cybersecurity, reinforcing efforts to combat deepfake threats and fostering improved trust and security across various sectors. © 2025 IEEE.

关键词： Long short-term memory

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Harnessing Big Data for Economic Transformation in a Digitalized World 3

Harnessing Big Data for Economic Transformation in a Digital...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Tarigan, Riswan Efendi Triyono Mahyuni Agustian, Harry Nurfaizi, Rifky Zahra, Achani Rahmania Az Pelita Harapan University Dept. of Information Systems Tangerang Indonesia University of Raharja Dept. of Information Technology Tangerang Indonesia University of Mataram Dept. of Teacher Training and Education Science Mataram Indonesia Sesindo Dept. of Computer Systems Tangerang Indonesia Bosindo Dept. of Information Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

Air is essential to life and health, with oxygen (20.94%) and the ozone layer protecting against ultraviolet radiation. Depletion of oxygen reduces the ozone layer, increasing UV exposure. Good air quality is crucial for living organisms. In 2022, Indonesia's PM 2.5 pollution ranked 26th worst globally, exceeding WHO standards, yet awareness and trust in air quality devices remain low. This study aims to implement and evaluate an AI-based machine learning system to predict the Air Quality Index (AQI) in healthcare services. Using Structural Equation Modeling with Partial Least Square analysis via SmartPLS 4.0, the research involves 120 participants and historical air quality data. Comparative analysis of machine learning algorithms assesses accuracy, robustness, and efficiency, highlighting their potential in forecasting AQI and its implications for public health and urban planning. The study demonstrates the effectiveness of these algorithms in predicting AQI, with a significant value of 0.874 for forecasting accuracy. © 2024 IEEE.

关键词： Ozone layer

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