DEEP FAKE DETECTION USING AI TECHNIQUES: A COMPREHENSIVE APPROACH

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

The increasing computational power will significantly enhance the capabilities of deep learning algorithms, making the creation of indistinguishable human-synthesized videos, commonly known as "deep fakes," quite straightforward. These deep fakes, featuring realistic face swaps, will be susceptible to misuse in scenarios involving political manipulation, fabricated terrorism incidents, revenge pornography, and extortion. In this future work, we will present an innovative deep learning-based approach for effectively identifying AIgenerated fake videos in contrast to genuine ones. Our approach will excel at automatically detecting deep fakes involving face replacement and reenactment, thereby utilizing Artificial Intelligence (AI) to combat the misuse of AI technology. Our system will employ a Res-Next Convolutional Neural Network to extract framelevel features. These extracted features will be subsequently utilized to train a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) for the classification of videos, determining whether they have undergone manipulation (i.e., whether they are deep fakes) or if they are authentic videos. To ensure that our model performs optimally in real-time scenarios, we will evaluate our method using a substantial dataset that combines various available datasets, such as Face-Forensic++, the Deepfake Detection Challenge, and Celeb-DF. We will also demonstrate how our system will achieve competitive results using a straightforward and robust approach.

- In recent months, the rise of free, deep learning- based software tools has significantly simplified the creation of highly realistic face-swapped videos, commonly known as "Deep Fakes" (DFs). While video manipulation through visual effects has been practiced for decades, recent advancements in deep learning have drastically enhanced the realism and accessibility of such synthetic content. Creating DFs using AI tools has become relatively straightforward; however, detecting these manipulations remains a major challenge. This is due to the complexity involved in training algorithms to recognize subtle and often imperceptible signs of tampering. In this work, we present a deep learning-based approach that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect Deep Fake videos. Our system employs CNNs to extract spatial features at the frame level, which are then passed to an RNN that captures temporal inconsistencies between frames introduced during Deep Fake generation. We evaluate our method on a large dataset of manipulated videos and demonstrate that our approach achieves competitive results using a relatively simple architecture. Key Words: Deep fake video detection, Convolutional neural networks(CNN), Recurrent neural networks(RNN)

Abstract—Deepfake technology, driven by generative adversarial networks (GANs), poses significant challenges in digital security, misinformation, and privacy. Detecting deepfakes in images and videos requires advanced deep learning models. This study explores deepfake detection using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures like Vision Transformers (ViTs). We employ Meso4_DF deepfake detection pipeline that uses TensorFlow/Keras, PyTorch, OpenCV for processing, with Dlib, Scikit-Image, and NumPy for feature extraction, leveraging transfer learning for enhanced accuracy. Video analysis includes frame extraction and temporal feature learning using LSTMs and 3D CNNs. Experimental results demonstrate that deep learning-based methods achieve high accuracy in distinguishing real and manipulated media, offering a robust approach to deepfake detection. Keywords—Deepfake Detection,CNN,GAN,Meso4 model, Image Analysis,Video Analysis.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as extracellular recording, which can offer scientists valuable information about diseases and neuron activities. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are significant components of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is essential. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough, as it involved extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. Recognizing noises from spikes produced by neural activity causes the spike classification task to bear a significant demand. Classifying spikes accurately and quickly reveals the role of AI in the scope of spike classification. This review provides an in‐depth discussion of the importance and use of AI in spike classification. This work organizes materials in the spike classification field for future studies and fully describes how spikes are recognized. Therefore, the existing datasets are described first. The topic of spike classification is then separated into three major components: preprocessing, classification, and evaluation. Each of these sections introduces existing methods and determines their importance. Having been summarized and compared, more efficient algorithms are highlighted. The primary goal of this work is to provide a perspective on spike classification for future research, as well as a thorough grasp of the methodologies and issues involved. In this work, numerous studies were extracted from various databases. The PRISMA‐related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. Although there are research papers on spike sorting using the keyword spike, the primary focus of this study is on spike classification. Finally, 47 papers were selected for in‐depth review. First, useful information on the datasets for these papers is supplied. In addition, preprocessing approaches, classification methods, and ultimate performance are investigated in each of these studies. The material is then summarized. Furthermore, the fundamental concerns regarding spike classification raised in the opening of this paper are thoroughly addressed throughout the review. Our reviewing outcomes illustrate that support vector machine and clustering‐based algorithms drastically influence machine learning methods in terms of high accuracy and many uses. Moreover, convolutional neural networks, spiky neural networks, and attention‐based techniques can classify spikes with considerable functionality among deep learning methods. Various preprocessing and classification techniques have been used practically to classify extracted signal data from patients in medical institutions. Our review emphasizes the importance of classifying spikes in neuroscience applications with machine learning and deep learning models. This can provide precious insights and hands‐on solutions for using AI to classify real‐world medical data. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Algorithmic Development > Biological Data Mining

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor treatment strategies to the unique genetic, environmental, and lifestyle characteristics of individual patients. This approach holds immense potential for improving therapeutic efficacy and minimizing adverse drug reactions. With the rapid advancement of artificial intelligence, deep learning has emerged as a transformative tool in pharmacology, enabling precise modeling of complex biological data and uncovering hidden patterns in patient-specific information. This study investigates the application of deep learning techniques - such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and Generative Adversarial Networks (GANs) - in optimizing personalized treatment strategies. Using a diverse dataset comprising electronic health records (EHRs), genomic sequences, and clinical indicators, we developed and trained deep learning models for tasks including drug response prediction, biomarker identification, and adverse drug reaction (ADR) forecasting. Among the models evaluated, Transformer-based architectures demonstrated superior performance, achieving an accuracy of 91.2% and an AUC-ROC of 0.92 in drug response prediction tasks. Moreover, the integration of deep learning models into the treatment pipeline resulted in a 20-30% improvement in drug-patient matching efficiency compared to traditional statistical methods. The findings underscore the potential of AI-powered systems to enhance clinical decision-making and enable precision pharmacotherapy. However, challenges such as data privacy, model interpretability, and regulatory compliance remain critical barriers to widespread adoption. The study also explores future directions, including the implementation of explainable AI (XAI) and federated learning, to address these limitations and facilitate the integration of deep learning into routine clinical practice.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Deepfakes, which are synthetic media realistic in nature generated using artificial intelligence (AI); pose a significant threat to individuals and society. The rapid advancement of deepfake technology has led to the creation of highly realistic synthetic content covering images, videos, audio, and news. While deepfake applications offer creative possibilities, their misuse for misinformation, identity fraud, and cybersecurity threats necessitates robust detection methods. Deepfake crimes are rising daily, wherein deepfake media detection has become a big challenge and has high claim in digital forensics. This review explores the state-of-the-art deep learning (DL) techniques for deepfake detection of four parameters, namely images, videos, audio, and news. The ML approaches rely on handcrafted features but struggle with evolving deepfake methods. In contrast, DL techniques, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have demonstrated superior detection accuracy by learning discriminative features. Even Recurrent Neural Networks (RNNs), and Transformer-based architectures like Bidirectional encoder representations from transformers (BERT), have demonstrated superior accuracy in identifying manipulated content. Furthermore, recent advancements such as Vision Transformers (ViTs) and Explainable AI (XAI) models are enhancing detection interpretability and robustness. This review highlights the future research directions for strengthening deepfake detection mechanisms. The rapid advancements in deepfake generation necessitate continuous research and development of countermeasures.

In order to explore the application of deep learning (DL) and artificial intelligence (AI) technologies in art education, this work proposes and optimizes an innovative art creation system—Creative Intelligence Cloud (CIC). The system combines a deep generative adversarial network and convolutional neural network, aiming to enhance the automation level, consistency of artistic styles, and creation efficiency in art creation. This work first analyzes existing art creation methods. It points out the shortcomings of traditional systems in terms of image quality, style transfer, and computational performance, especially the application limitations in real teaching scenarios. Therefore, this work designs an art creation model optimized by DL and validates and evaluates it through extensive experiments. The experimental results show that CIC outperforms existing mainstream models in multiple dimensions, including image quality, computational performance, user experience, and style creation. For example, in image quality evaluation, CIC achieves high scores in clarity (0.89), detail performance (0.85), style consistency (0.87), and color accuracy (0.91). In terms of computational performance and resource consumption, CIC shows its superiority, with a training time of only 1500 s, memory consumption of 4.9GB, and a Graphics Processing Unit resource usage rate of 70%. Compared to models such as the Visual Perception Generative Adversarial Network and Artistic Recognition and Transfer Style Convolutional Neural Network, CIC is more efficient and consumes fewer resources. Furthermore, CIC’s scores in user experience and style transfer capability are significantly higher than those of other models, providing smoother and more creatively rich art creation tools for art education. Therefore, this work offers new ideas and methods for the application of DL and AI technologies in art creation and art education, and promotes the practical use of AI in art education. The work has certain academic contributions and practical value.

The artificial intelligence (AI) is creating the great impacts and revolution in the technological world, education world, industry world, and business world. The concepts in artificial intelligence are improving themselves day by day, and it is becoming a part of human’s everyday activities. It plays a major role in the human life, and the situation becomes the people cannot live without this AI. In this book chapter, the categorization of artificial intelligence based on the autonomous vehicles and its other applications is to be discussed in brief. The artificial intelligence technique is based on the artificial neural network (ANN), machine learning (ML), deep neural network (DNN), recurrent neural network (RNN), and convolution neural network (CNN). To implement these types of neural network, the system should use the programming languages like MATLAB programming, Python programming, R programming, etc.

Disease diagnosis is the identification of a health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could sometimes be very easy, while others may be trickier. There are large data sets available however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods that are used to diagnose a disease are manual and error-prone. Using artificial intelligence (AI) predictive techniques enables automatic diagnosis and reduces detection errors compared to exclusive human expertise. This paper has reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered the eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify the most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges. So, in our project titled “Diagnosis of acute diseases using AI” is used to improve the diagnostic capabilities using AI. Here we have used four algorithms namely: Autoencoders, Collaborative Filtering, Reinforcement Learning, and Generative Adversarial Network. And at last, we have done a comparative analysis of all four algorithms to find which algorithm is more accurate. INDEX TERMS: AI Techniques, Deep Learning, Intelligent systems, Generative Adversarial Network ,autoencoders ,collaborative filtering, Reinforcement Learning