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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency 44

Comparing Impact of Time Lag and Item Lag in Relative Judgme...

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44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Maini, Sahaj Singh Labuzienski, Louis Gulati, Saurabh Tiganj, Zoran Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Judgment of Recency Time versus items Variable presentation time

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Long Short-Term Memory Auto-Encoder-Based Position Prediction Model for Fixed-Wing UAV during Communication Failure

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 IEEE.

关键词： Real time systems

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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Comparative Modeling of CPU Execution Times of AES and ECC-AES Hybrid Algorithms Over Mobile Cloud Environment 10th

Comparative Modeling of CPU Execution Times of AES and ECC-A...

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10th International Conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2022

作者： Thomas, Ambili Lakshmi Narasimhan, V. Department of Information Technology Botho University Gaborone Botswana Department of Computer Science Paulson College of Engineering and Computing Statesboro United States

ISBN: (纸本)9789811975233

This paper presents an extended comparative study on the modeling of CPU execution times for AES and a hybrid encryption algorithm AES-ECC. The encryption key is generated using the ECC algorithm, and then, this key is passed to the AES algorithm to encrypt the content of a data file. The ECC key integration with the AES encryption algorithm results in substantial increase in the security of the encrypted data. However, this study shows that the hybrid encryption algorithm utilizes more CPU execution time to encrypt the data file, when compared to the AES algorithm. Applications which demand high security are suggested to use the hybrid algorithm, whereas applications which demand faster execution are suggested to use the AES algorithm. The performance metric used for this study is the CPU execution time and is calculated using the C#.net framework and the assembly code. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile cloud computing

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation 11

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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An Efficient Classification of Cassava Leaf Disease using VGG 16

An Efficient Classification of Cassava Leaf Disease using VG...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Soujanya, A. Cherukuvada, Srikanth Murugan, V. Ramya, Singaraju Agalya, K. Gayathri, S. Ibrahimpatnam India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India CARE College of Engineering Department of Artificial Intelligence and Data Science Thayanur Trichy India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Samayapuram Trichy India

ISBN: (纸本)9798350319200

Cassava is an essential food item in the tropics, it may origin enormous productivity decreases in cassava, leading to significant financial losses and a shortage of basic foods. Small-scale farmers grow the essential food and nutrition crop cassava because it can withstand harsh conditions. In many African nations, it provides a substantial source of carbohydrates. Leaf infections can occasionally harm cassava crops, limiting total productivity and farmers' income. In this study, Pre trained models are utilized for the determination of disease identification and classification on plant leaf issues. VGG 16 has been put into use, and its performance has been examined. An exploratory study is carried out using an online Cassava image collection, which contains pictures of Cassava leaves afflicted with five different disease categories. The vgg16 classifier that was suggested surpasses its competitors greatly and achieves an accuracy of 97.58%. The results of the test demonstrate that improving classification performance by using a database of images that is well balanced is possible. © 2023 IEEE.

关键词： Classification CNN Deep learning Transfer learning VGG16

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Towards Enhanced Deep CNN for Early and Precise Skin Cancer Diagnosis 1

Towards Enhanced Deep CNN for Early and Precise Skin Cancer ...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Malaiarasan, S. Ravi, R. Maheswari, D.R. Yesubai Rubavathi, C. Ramnath, M. Hemamalini, V. Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Thamirabharani Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India School of Computing SRMIST KTR Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Most people's first experience with cancer will be with skin cancer, which is also the most prevalent and potentially fatal kind. Determining a skin cancer diagnosis also requires the use of information technologies. This highlights the need of developing and deploying highly effective deep-learning methods for the early and accurate diagnosis and detection of skin cancer. Deep Convolution Neural Network (DCNN) is proposed for automated skin cancer detection in this study. This study's unique contribution is the use of a deep convolution neural network containing 12 nested processing layers to improve the accuracy of skin cancer diagnosis and detection. As a consequence of this study's findings, researchers have determined that deep learning techniques are superior to machine learning for spotting skin cancer. As a consequence, pathologists' precision and competence may be improved by using automated evidence-based detection of skin cancer. To accurately distinguish between benign and malignant skin lesions, we present a deep convolution neural network (DCNN) model in this research that uses a deep learning technique. First, we normalize the input photos and identify characteristics that aid in correct classification, then we apply a filter or Gaussian to eliminate noise and artifacts, and lastly, we supplement the data to increase the number of images, which enhances the accuracy of the classification rate. © 2023 IEEE.

关键词： Image enhancement

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