Fake Content Research Articles

Pandemic Experts. On the Transformation of Values and Evaluations in the Practice of Science and its Science Educational Dissemination in the Media in Poland During the Covid-19 Pandemic

The SARS-CoV-2 coronavirus pandemic has forced changes in many areas of human life activity, also in the field of science, educating students and popularization knowledge. These changes were avalanche-like. Sudden increase in the incidence and introduction due to the pandemic situation, regulations at both national and university levels caused impossiblity to prepare for changes in the areas related to science. They had to be dradually introduced. The aim of this article is to show the changes in the area of values in the practice of science and its dissemination in the media. Tools that worked well during the pandemic are still being used despite cancellation of the epidemic threat, of course to a lesser extent. In in the area of dissemination of expert knowledge, solutions adopted during successive waves pandemics are still applied; experts are invited to the studio, but they often connect with editorial staff using various communicators to disseminate via the media knowledge of your discipline. With the transfer of the work of academics to the Internet, both related to conducting research, educating students, and disseminating knowledge in the media were associated with a number of problems, the most serious in the area of research, but also strongly felt in the area of education. In turn, in the area popularization of science, dissemination of expert knowledge, these changes are strongly related to changes that have been introduced due to the pandemic situation in the media electronic, and have brought many benefits. The biggest of them seems to be expanding the group media experts about dormitories from outside the town whose TV stations or radio stations have a professional studio. In a situation of great demand for information on the SARS-CoV-2 virus and ways to prevent the disease, medical experts and professionals shared their knowledge in the media and straightened it out via communicators fake content spread by disinformers.

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.

DeepFake Deception: A Comprehensive Analysis of DeepFake Technology and its Effects on Ethics, Politics and Society

Abstract: Our research examines the tremendous effects of deepfake technology on politics, culture, and ethics in the modern day. This investigation is comparable to the spread of false information. Our research aims to thoroughly evaluate the impact of deepfake technology. We explore its moral ramifications, the possibility that it may sway political debate, and its larger societal repercussions. We undertake data analysis using open-source tools like Python and Power BI, producing a range of visual representations including charts and Word Clouds. We create and carry out a unique survey to track the penetration of viral deepfake material in various countries. We carefully consider variables like the kinds of deepfake disinformation, the reasons for their fabrication, and the channels through which they are disseminated. In addition to circumstances analogous to the setting of deepfake deception, our study offers useful insights that can guide the development of mitigation solutions for disinformation issues across a variety of areas. Keywords—Deepfake Technology, Disinformation, survey analytics, Fake Content, Misinformation, Deception

Fakenews is much more than fake content

The goal of this article is simple. In the context in which there is more and more talk about the concept of fakenews, there are also efforts to instrumentalize and operationalize it. Rightly so, from media to commerce, from social networks to propaganda, the phenomenon is at the heart of the communication processes we use today, but also at the heart of the problems we face as societies. Unfortunately, fakenews is a much more subtle concept than people think and the big question is whether this phenomenon can be approached legally, without violating the right to opinion and free expression. Our article deals precisely with these subtleties of the concept and puts them in parallel with the normative basis that we now have as a reference. What can and cannot be done in this area ultimately depends on our ability to correctly understand the manifestations of what we call fakenews.

Problems of consumer behavior transformation as a result of the negative impact of deepfakes technology on modern digital society

The relevance of the topic of the study stems from the fact that modern digital technology has a negative impact on consumer behaviour and makes it difficult to distinguish between real and fake media. The latter, in turn, are detrimental to society. One of the developments contributing to institutional media problems has been the emergence of deepfakes, which are hyper-realistic “fake content” videos that use artificial intelligence. Combined with the reach and speed of social media, persuasive deepfakes are reaching millions of people in a short period of time and have a negative impact on our society. The paper analyzes the technology of creating and using deepfakes, describes the results of its impact on consumer behaviour, identifies types of deepfakes and groups of producers, and identifies advantages and threats of this technology.

Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news sources based on human judgments and opinions. We then extract discriminating linguistic features commonly found in source domain news using advanced deep learning models. We transfer these features associated with the source domain to augment fake content detection in three target domains: political news, financial news, and online reviews. We show that domain invariant linguistic features learned from a source domain with abundant labeled examples can effectively improve fake content detection in a target domain with very few or highly unbalanced labeled data. We further show that these linguistic features offer the most value when the level of transferability between source and target domains is relatively high. Our study sheds light on the platform operation in managing online content and resources when applying machine learning for fake content detection. We also outline a modular architecture that can be adopted in developing content screening tools in a wide spectrum of fields.

A Robust Approach to Multimodal Deepfake Detection.

The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish between authentic and fake media. Nonetheless, though deepfake generation systems can create convincing images and audio, they may struggle to maintain consistency across different data modalities, such as producing a realistic video sequence where both visual frames and speech are fake and consistent one with the other. Moreover, these systems may not accurately reproduce semantic and timely accurate aspects. All these elements can be exploited to perform a robust detection of fake content. In this paper, we propose a novel approach for detecting deepfake video sequences by leveraging data multimodality. Our method extracts audio-visual features from the input video over time and analyzes them using time-aware neural networks. We exploit both the video and audio modalities to leverage the inconsistencies between and within them, enhancing the final detection performance. The peculiarity of the proposed method is that we never train on multimodal deepfake data, but on disjoint monomodal datasets which contain visual-only or audio-only deepfakes. This frees us from leveraging multimodal datasets during training, which is desirable given their lack in the literature. Moreover, at test time, it allows to evaluate the robustness of our proposed detector on unseen multimodal deepfakes. We test different fusion techniques between data modalities and investigate which one leads to more robust predictions by the developed detectors. Our results indicate that a multimodal approach is more effective than a monomodal one, even if trained on disjoint monomodal datasets.

Identifying Fake News via Machine Learning and Web Scrapping

Abstract: After digitalization, the increase in use of social media has led the flow of information among the social network users unchecked. The news is immediately transported to social networks, where it is quickly read, marked with opinions (on Facebook), and shared (on Twitter and Facebook) without being verified as accurate or false numerous times. In the modern world, fake news is an issue that has grown difficult to detect. Its impact on the judgement of commonfolk is noticeable, society has witnessed numerous events where the rampant flow of unverified news had affected the society as a whole. The sharing of fake news has become a major issue to the society. Our project aims to aid in reduction of e-crimes like use of social media for sharing of fake content. This will be achieved by training the machine to identify text based fake content. Using this the time for work of filtering out the fake content will be reduced by great lengths whilst helping the mass to get verified and credible news.

Quick Overview of Face Swap Deep Fakes

Deep Fake technology has developed rapidly in its generation and detection in recent years. Researchers in both fields are outpacing each other in their axes achievements. The works use, among other methods, autoencoders, generative adversarial networks, or other algorithms to create fake content that is resistant to detection by algorithms or the human eye. Among the ever-increasing number of emerging works, a few can be singled out that, in their solutions and robustness of detection, contribute significantly to the field. Despite the advancement of emerging generative algorithms, the fields are still left for further research. This paper will briefly introduce the fundamentals of some the latest Face Swap Deep Fake algorithms.

“ALL TRUTH IS RELATIVE” OR HOW TO NOT BE FOOLED IN THE POST-TRUTH AGE?

The term “fake news” is not a new phenomenon. Before the advent of the Internet, people read news from reliable sources which were required to adhere to strict standards of professional ethics. However, due to the ease of access to the Internet and social media there have emerged new ways of publishing, sharing and consuming news and information, in the meanwhile, the control over the quality and editorial norms have declined significantly. This has changed the way fake news is created and distributed. People now prefer to read the information on social media and other online sources. However, it is not always easy to determine which content is true and which is not. The paper argues that fake news employs specific language and graphic patterns which are meant to generate an emotional response. Knowing the language features of fake news may help readers become less susceptible to fake content. The study is built on the hypothesis that a critical analysis of how the information is represented activates a process of reasoning that helps spot fake content.

Using Twitter Data Analysis to Understand the Perceptions, Beliefs, and Attitudes about Pharmacotherapy Used in Rheumatology: An Observational Study.

Twitter has become an important platform for disseminating information about rheumatology drugs by patients, health professionals, institutions, and other users. The aim of this study was to analyze tweets related to 16 drugs used in rheumatology, including their volume, content, and type of user (patients, patients' relatives, health professionals, health institutions, pharmaceutical industry, general press, scientific journals and patients' associations), and to detect inappropriate medical content. A total of 8829 original tweets were obtained, with a random sample of 25% of the total number of tweets for each drug (at least 100 tweets) analyzed. Methotrexate (MTX) accounted for a quarter of all tweets, and there were significant differences in the proportion of tweets issued according to the type of user. Patients and their relatives mainly tweeted about MTX, while professionals, institutions, and patient associations posted more about TNF inhibitors. In contrast, the pharmaceutical industry focused on IL-17 inhibitors. Medical content prevailed in all drugs except anti-CD20 and IL-1 inhibitors and the most discussed medical topic was efficacy, followed by posology and adverse effects. Inappropriate or fake content was found to be very low. In conclusion, the majority of the tweets were about MTX, which is a first-line treatment for several diseases. The distribution of medical content varied according to the type of user. In contrast to other studies, the amount of medically inappropriate content was very low.

Interpretable fake news detection with topic and deep variational models

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-the-art competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings.

Deep fake detection and classification using error-level analysis and deep learning

Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The results prove the efficiency and robustness of the proposed technique; hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda.

On the Generalization of Deep Learning Models in Video Deepfake Detection.

The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as "deepfakes", is making it more challenging to differentiate between real and fake content, while various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set. In this study, we carry out an analysis of different deep learning architectures in an attempt to understand which is more capable of better generalizing the concept of deepfake. According to our results, it appears that Convolutional Neural Networks (CNNs) seem to be more capable of storing specific anomalies and thus excel in cases of datasets with a limited number of elements and manipulation methodologies. The Vision Transformer, conversely, is more effective when trained with more varied datasets, achieving more outstanding generalization capabilities than the other methods analysed. Finally, the Swin Transformer appears to be a good alternative for using an attention-based method in a more limited data regime and performs very well in cross-dataset scenarios. All the analysed architectures seem to have a different way to look at deepfakes, but since in a real-world environment the generalization capability is essential, based on the experiments carried out, the attention-based architectures seem to provide superior performances.

VIOLA jones algorithm with capsule graph network for deepfake detection.

DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones's (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

Playability of Serious Games in Information Literacy: The Findings of the European Project NAVIGATE

The article deals with the playability of serious games in information literacy applied by academic libraries and university departments in Library and Information Sciences in the training sessions with students. Is it possible these games to be more playful and what is path we must follow to achieve this goal? In the literature review on the concepts of playfulness/gamefulness we are focusing on two opposite sides of a spectrum presented by Roger Caillois: Paidia (or playing; for pure joy, without any goals) and Ludus (or gaming; based on rules and competition). The author’s view is that the games can be at either end or somewhere in the middle (Caillois & Barash, 2001). We have a hypothesis that the serious games in information literacy can never be only playful but if they contain a higher level of playability it will be easier for the students to achieve the learning objectives set by the university teachers and librarians. In the framework of the European project NAVIGATE – Information Literacy: A Game-based Learning Approach for Avoiding Fake Content (https://navigateproject.eu) 70 games used for teaching information literacy in academic libraries and programs were identified and evaluated. We have selected among them 20 best examples of such games and ranked them according to the following criteria: Playability, Lastability, Engagement, User Interface, and Storytelling. An interactive database (https://www.navigateproject.eu/navigamesearch-tool/) was created in order to visualize the list and the categories (the NaviGAMESearcher). Taking into account the results of the evaluation of the top information literacy games and the two original digital games developed within the project (Information Trap Manager and the Navigator) we analyzed where are these games located on the scale of playfulness versus gamefulness and what is the correlation between the level of playability of these games and the students’ achievements. The further steps for evaluation of information literacy games according to the Playful Experiences (PLEX) framework were also defined in the article.

Are Fake Images Bothering You on Social Network? Let Us Detect Them Using Recurrent Neural Network

Nowadays, social media platforms play a significant role in real-world events, which can cause both positive and negative effects. The popularity of image-based content on social media has been dramatically increased, which brings the problem that the quality of content is rather spotty. Therefore, automated techniques of identifying fake images have drawn significant attention. The traditional detection methods focus on the elements’ consistency of the image, which requires massive computing resources and huge datasets for pairs of real and fake images. Many studies on detecting rumors on social media showed that there are propagation patterns for the spreading of fake content that can be used as clues of detection. Thus, the proposed approach attempts to characterize the propagation patterns of fake images on social media using several user features and tweet features. The detection model applies recurrent neural networks to capture the variation of suggested features along the propagation path over time. The results of the experiment on a Weibo dataset of image tweets show that the model can achieve 89% accuracy in classifying fake images from real ones. Moreover, the model already reaches high performance as the detection deadline is smaller than 24 h, which demonstrates the strong capability of early detection. The positive outcomes indicate that the proposed detection model has great potential to be further developed to an automated technique that can be used in classifying real images from fake images posted on social media.

Deep learning approaches for robust deep fake detection

Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As more deepfakes are spreading, we really need better ways to find and prevent them. Deepfake involves creation of highly realistic images and videos and misuse them for spreading fake news, defaming individuals, and possess a significant threat to the integrity of digital content. Our project “Deep Learning Approaches for Robust Deep Fake Detection” aims to address this critical issue by developing a robust system for identification and localization of deep fake content by using ‘Densenet121’ model. This proposed framework seamlessly integrates forgery detection and localization. The dataset used in this project is “140k Real and Fake Faces”, and it consists of 70k real faces from Flickr dataset collected by Nvidia and 70k fake faces sampled from the 1 million Fake faces generated by StyleGAN. For localization purpose, we use GRAD-CAM method to accurately identify the morphed regions. Overall, our goal is to make deepfake detection more effective and reliable in today’s digital landscape.

Fake paper factories: root cause solutions are needed.

The pressure to publish academic papers for career advancement and technological innovations have fostered the growth of a new phenomenon, the serial fabrication of articles to be submitted to scientific journals. Papers with fake content are produced by specialised publishing agencies. Journal editors and publishers increased vigilance and control, tightening the peer review process and using anti-plagiarism software. But, being a systemic issue, it is not easy to find the solution and action must be taken on its causes: greater awareness of research ethics is needed, specific training for young doctors and - in the long run - an appropriate use of artificial intelligence, which should become a tool for protecting the integrity of knowledge.

CRIMINAL LIABILITY OF NOTARY IN CRIMINAL ACT COMMITTED BY NOTARY SIGNING AGENT

This research aims to analyze and examine how the responsibility of a notary in the case of a notary's signing agents commits the crime of document forgery. Article 1 paragraph (1) of Notary Office Law states that a Notary is a Public Official authorized to do authentic deeds and has other authorities as referred to in this Law or based on other laws. The notaries get authority attribution from the state through the Law on Notary Positions. This authority is attached to the position of a notary. In conducting these duties and positions, a Notary is generally assisted by Notary signing agents. In this research, Normative Law is used because it tries to find the location of coherence, namely legal rules and legal norms. Notary workers are only assistants in their work, and the responsibility for authentic deeds remains to the Notary. When the Notary signing agents commit document forgery, resulting in defects in the authentic deed, it is possible that Notary must hold responsible for this. The forgery of letters can occur due to fake contents of the letter or fake authority and contents of authority in the letter. The forms of Notary Criminal Liability, if it is proven that the notary worker committed the crime of forging letters, is a crime of participation in the crime of forging letters because the Notary is considered negligent in conducting his duties and position.