An Investigation of the Effectiveness of Deepfake Models and Tools

Mukta, Md. Saddam Hossain; Ahmad, Jubaer; Raiaan, Mohaimenul Azam Khan; Islam, Salekul; Azam, Sami; Ali, Mohammed Eunus; Jonkman, Mirjam

doi:10.3390/jsan12040061

Cited by 16 publications

(3 citation statements)

References 124 publications

(163 reference statements)

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“…Enabling Data Analysis and Information Extraction: LLMs play a pivotal role in extracting valuable insights from unstructured text data [159]. This is particularly critical in fields like finance, market research [160], and healthcare, where deciphering market trends, sentiment in news, or medical records holds paramount significance.…”

Section: Industrial Significance Of Large Language Modelsmentioning

confidence: 99%

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Raiaan,

Mukta,

Fatema

et al. 2024

IEEE Access

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Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in realworld scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.

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Section: Industrial Significance Of Large Language Modelsmentioning

confidence: 99%

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Raiaan,

Mukta,

Fatema

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…LLMs play a pivotal role in extracting valuable insights from unstructured text data [158]. This is particularly critical in fields like finance, market research [159], and healthcare, where deciphering market trends, sentiment in news, or medical records hold paramount significance.…”

Section: Enabling Data Analysis and Information Extractionmentioning

confidence: 99%

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Raiaan,

Mukta,

Fatema

et al. 2023

Preprint

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<p>Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLM training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world aspects, including ethical issues, model biases, computing resources, interoperability, contextual constraints, privacy, security, etc. It also discusses methods to improve the robustness and controllability of LLMs. Finally, the study analyses the future of LLM research and issues that need to be overcome to make LLMs more impactful and reliable. However, this review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals. Furthermore, it serves as a valuable reference for future development and application of LLM in numerous practical domains.</p>

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“…The surveyed literature classifies detection methods into four categories: deep learning, classical machine learning, statistical, and blockchain-based techniques. Meanwhile, (3) delves into the creation and detection of deepfakes using AI and machine learning. This research introduces a Python-implemented hybrid architecture that combines ResNet50 and LSTM for enhanced deepfake video detection, evaluated on datasets like Celeb-DF and Face Forensic++.…”

Section: Introductionmentioning

confidence: 99%

ResNet50 DeepFake Detector: Unmasking Reality

Borade,

Jain,

Patel

et al. 2024

IJST

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Objectives: The objective of this research is to detect video deepfakes with a higher accuracy and provide optimum results. The research aims to reduce time complexity for the media processing while simultaneously working on the model accuracy. Methods: This research has utilized CelebDF and FaceForensics++ Datasets for training and 32 epochs with the use of Single Nvidia Tesla T4 GPU. The above method of training and validating the model yielded error of <5% and is very capable. Using image scraping this model initially eliminates the unimportant areas of consideration. Thus, reducing the amount of scans that the model does in order to identify the face. This reduces that training to graph ratio and provides less error margin. Findings: This research’s findings reveal the model's robustness in detecting manipulated videos generated by deepfake techniques. Through extensive experimentation on diverse datasets, ResNet50 consistently demonstrated 97% accuracy, sensitivity, and specificity in distinguishing authentic content from deepfakes. The model exhibited exceptional generalization across various scenarios, including face-swapping and lip-syncing, showcasing its adaptability to evolving deepfake techniques. This research contributes to the already existing literature on ResNet50 deepfake detection tools. It contributes by adding the image scraping feature to the ResNet50 model and overcomes the gaps such as increasing error percentage of some of the models. The research of 1 has a 20% error percentage while this research has an error percentage of 5% with an accuracy of 97%. Novelty: The study employs ResNet50 to detect deepfake videos, utilizing novel image scraping techniques to minimize errors and enhance prediction accuracy. Keywords: Deepfakes, Deep Learning, GAN, ResNet50, FaceForensics++, CelebDF

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An Investigation of the Effectiveness of Deepfake Models and Tools

Cited by 16 publications

References 124 publications

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

ResNet50 DeepFake Detector: Unmasking Reality

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