Due to the fast spread of data through digital media, individuals and societies must assess the reliability of information. Deepfakes are not a novel idea but they are now a widespread phenomenon. The impact of deepfakes and disinformation can range from infuriating individuals to affecting and misleading entire societies and even nations. There are several ways to detect and generate deepfakes online. By conducting a systematic literature analysis, in this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos), and how these approaches can be employed within different situations to counter the spread of deepfakes and the generation of disinformation. Moreover, we explore state-of-the-art frameworks related to deepfakes to understand how emerging machine learning and deep learning approaches affect online disinformation. We also highlight practical challenges and trends in implementing policies to counter deepfakes. Finally, we provide policy recommendations based on analyzing how emerging artificial intelligence (AI) techniques can be employed to detect and generate deepfakes online. This study benefits the community and readers by providing a better understanding of recent developments in deepfake detection and generation frameworks. The study also sheds a light on the potential of AI in relation to deepfakes.

Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence

In this research, we propose a method that utilizes machine learning to maintain the parallelism of the resonant cavity in a Fabry–Perot interferometer designed specifically for glass substrates. Based on the optical principle and theory, we establish a proportional relationship between interference fringes and the inclination angle of the mirrors. This enables an accurate determination of the inclination angle using supervised learning, specifically classification. By training a machine learning model with labeled data, interference fringe patterns are categorized into three levels, with approximately 100 training data available for each level in each location. The experimental results of Level 2 and Level 3 classification indicate an average number of corrections of 2.55 and 3.55 times, respectively, in achieving the target position with a correction error of less than 30 arc seconds. These findings demonstrate the essential nature of this parallelism maintenance technology for the semiconductor industry and precision mechanical engineering.

/pdf/developing-an-interferogram-based-module-with-machine-1xl07bbv.pdf

Developing an Interferogram-Based Module with Machine Learning for Maintaining Leveling of Glass Substrates

Deepfake detection is an active area of research due to extensive use of deepfake media for spreading false information, manipulate public opinion and cause harm to individuals.This paper presents a critical and systematic review of 84 articles for deepfake generation and detection.We review the current state-of-the-art techniques for deepfake detection techniques by grouping them into four different categories: deep learning-based techniques, traditional machine learning-based, artifacts analysis-based and biological signal-based methods, the datasets used for training and testing deepfake detection models.We also discuss the evaluation metrics used to measure the effectiveness of these methods and the challenges and future directions of deepfake detection research.Our findings suggest that deep learning models demonstrate superior accuracy compared to other methods and artifacts analysis-based methods shows greater potential in precision but there is still room for improvement in detecting more sophisticated and realistic deepfakes. 

Exploring Deepfake Detection: Techniques, Datasets and Challenges

Artificial Intelligence and New Threat Vectors: Using Scenario Planning for Trend Forecasting

With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes.

AFMB-Net

Face Recognition is one of the premier disciplines in the vast field of computer vision and image analysis. A popular method is the Gaussian scale space analysis which limits the performance by smoothing both the noise and natural boundaries in the same proportion. In order to prevent the loss of natural boundaries to smoothing we tap into nonlinear scale space techniques such as KAZE and Accelerated KAZE. KAZE is a multistage 2-D feature detection and description algorithm. It makes use of AOS schemes to develop the nonlinear scale space for analysis. Though the results are satisfactory, it is computationally intense as they solve a humongous module of linear equations. To ascertain the mentioned limitation of KAZE we make use of Accelerated-KAZE, which uses pyramidal structure with Fast Explicit Diffusion incorporated in it, thus minimizing the computation in the step of feature detection of nonlinear scale space. Also, with the use of M-LDB (Modified-Local Difference Binary) descriptor the problem of rotation is solved. Usage of RANSAC after processing in two methods had some disadvantages. It gave bad results when the inliers ratio in the dataset is low. Thus, Optimal RANSAC is employed which works well even when the inliers ratio is as low as 5%. The proposed methods are tested on many standard datasets and various performance parameters. 

Optimal KAZE and AKAZE Features for Facial Similarity Matching

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AFMB-Net

Optimal KAZE and AKAZE Features for Facial Similarity Matching