Unmasking Deepfakes: A Comprehensive Review of Deep Learning-Based Detection Methods

Deepfakes, a term combining "deep learning" and "fake," refer to synthetic media where a person's likeness in an image or video is replaced with someone else's. These manipulations present significant ethical, privacy, and security challenges. This comprehensive review explores various deep learning-based methods used to detect deepfakes, highlighting their evolution, strengths, and limitations. We delve into the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and capsule networks (CapsNets) in detecting these forgeries. Key evaluation metrics, notable datasets, and persistent challenges in the field are discussed. The review concludes by identifying future directions in deepfake detection, emphasizing the need for robustness, real-time capabilities, and model explainability to effectively combat the rise of deepfakes

Next Generation AI-Based Firewalls: a Comparative Study

Cybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitive information and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) that integrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. As traditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promising avenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-based firewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of 20-25 references from reputable sources, including ijcseonline.org, forms the basis for this comparative study. The selected references provide insights into various AI-based firewall architectures, algorithms, and performance metrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematic approach employed to compare different AI-based firewall methods. Leveraging machine learning and deep learning approaches, the study assesses key performance metrics such as detection accuracy, false positive rates, and computational efficiency. The goal is to provide a nuanced understanding of the strengths and weaknesses inherent in each approach, facilitating an informed evaluation. The comparative analysis section employs graphical representations to elucidate the findings, offering a visual overview of the performance disparities among selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholders with valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to the ongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements that fortify the cybersecurity landscape

Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets

Deep Neural Network Solution for Detecting Intrusion in Network

In our experiment, we found that deep learning surpassed machine learning when utilizing the DSSTE algorithm to sample imbalanced training set samples. These methods excel in terms of throughput due to their complex structure and ability to autonomously acquire relevant features from a dataset. The current study focuses on employing deep learning techniques such as RNN and Deep-NN, as well as algorithm design, to aid network IDS designers. Since public datasets already preprocess the data features, deep learning is unable to leverage its automatic feature extraction capability, limiting its ability to learn from preprocessed features. To harness the advantages of deep learning in feature extraction, mitigate the impact of imbalanced data, and enhance classification accuracy, our approach involves directly applying the deep learning model for feature extraction and model training on the existing network traffic data. By doing so, we aim to capitalize on deep learning's benefits, improving feature extraction, reducing the influence of imbalanced data, and enhancing classification accuracy

A Survey of Deep Learning Approaches for Natural Language Processing Tasks

In recent years, deep learning has been a go-to method for solving difficult NLP problems. Deep learning models have attained state-of-the-art performance across a wide range of natural language processing applications, including text summarization, sentiment analysis, named entity identification, and language translation, by utilizing enormous neural network designs and massive volumes of training data. In this paper, we take a look at the most important deep learning methods and how they've been used for different natural language processing jobs. We go over the basics of neural network designs including CNNs, RNNs, and transformers, and we also go over some of the more recent developments, such as BERT and GPT-3. Our discussion of each method centers on its guiding principles, benefits, drawbacks, and significant NLP applications. To further illustrate the relative merits of various models, we also provide their comparative performance findings on industry-standard benchmark datasets. We also highlight some of the present difficulties and potential future avenues of study in deep learning applied to natural language processing. The purpose of this survey is to offer academics and practitioners in natural language processing a high-level perspective on how to make good use of deep learning in their respective fields

Utilizing Deep Learning for Automated Inspection and Damage Assessment in Civil Infrastructure Systems

The integrity of civil infrastructure systems, including bridges, roads, tunnels, and buildings, is critical for public safety and economic stability. Traditional methods of inspection and damage assessment often rely on manual visual inspections, which can be time-consuming, subjective, and prone to errors. With advancements in deep learning, there is an opportunity to revolutionize the inspection and damage assessment processes through automated systems that offer increased accuracy, efficiency, and scalability. This paper explores the application of deep learning for automated inspection and damage assessment in civil infrastructure systems. We analyze various deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and their roles in defect detection, damage classification, and structural health monitoring. We also discuss the challenges associated with implementing these technologies, such as data quality, model interpretability, and integration with existing infrastructure. By addressing these challenges, deep learning can significantly enhance the capabilities of automated inspection systems, leading to more reliable and timely assessments of infrastructure health