Deepfakes

The rise of deep fake technology has ignited widespread societal apprehensions about potential security risks and the dissemination of false information. Despite extensive research into deepfake detection, effectively discerning low-quality deepfakes and simultaneously identifying variations in their quality remains a significant and formidable challenge. This investigation explores the dynamic field of deep fake detection, focusing specifically on video analysis targeting facial manipulations. The study introduces Celeb-DF, a substantial dataset comprising high- quality deep fake videos of celebrities, challenging prevailing detection methods. Additionally, a revolutionary Quality-Agnostic Deep Fake Detection (QAD) framework addresses the intricate task of simultaneously recognizing diverse qualities of deep fakes, surpassing established benchmarks across multiple datasets. The paper highlights ongoing efforts to enhance deep fake detection strategies, incorporating advanced models such as Stable Diffusion, and employing interpretability-through-prototypesn by merging fine-tuned Vision Transformers with Support Vector Machines. Keywords -- Deep fake technology, security risks, deep fake detection, Celeb-DF dataset, video analysis, Quality-Agnostic Deep Fake Detection (QAD), Stable Diffusion, Vision Transformers, Support Vector Machines.

Deep fakes, synthetic media generated through artificial intelligence (AI) techniques, have become a growing concern due to their potential for misinformation, manipulation, and security threats. As the sophistication of deep fake technology continues to evolve, traditional methods of detection are becoming less effective, thus prompting the need for advanced AI-driven solutions. This article explores the various AI techniques employed in deep fake detection, such as deep learning, neural networks, and computer vision algorithms. A review of the latest advancements in AI technologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), is presented to highlight their roles in identifying deep fakes. Additionally, we explore challenges related to data availability, model robustness, and ethical implications. The paper concludes by proposing a hybrid detection framework that integrates multiple AI techniques to enhance the accuracy and efficiency of deep fake identification.

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 Deep Face Live is a cutting-edge application designed to revolutionize the field of digital media manipulation by enabling seamless face replacement in videos. Leveraging state-of-the-art deep learning models and algorithms, Deep Face Live empowers users to create compelling and realistic content with unprecedented ease and efficiency. This research paper presents a comprehensive overview of Deep Face Live, including its architecture, functionalities, and applications. We discuss the methodology employed in the development of Deep Face Live, highlighting key design decisions, algorithmic approaches, and technical considerations. Furthermore, we evaluate the performance of Deep Face Live through qualitative and quantitative analysis, demonstrating its effectiveness in generating high-quality deep fake content. Additionally, we address ethical considerations surrounding the use of Deep Face Live and propose strategies for responsible deployment and usage. Overall, this research paper aims to contribute to the advancement of digital media technology and foster informed discourse on the ethical implications of Deep Face Live. Index Terms : Deep learning, Deep Fake, review, Deep Fake generation, Deep Fake creation

This chapter examines data protection and privacy laws, the impact of Artificial Intelligence (AI) on democratic processes, and the associated ethical and legal challenges. It analyzes prominent regulatory frameworks such as the European Union's GDPR and the fragmented legislation in the United States. The opportunities and threats of AI in democratic processes, including increased transparency and citizen engagement versus challenges like fake news and deep fakes, are explored. The study highlights the need for cohesive regulations to ensure individual rights and uphold democratic principles in the face of technological advancements.

Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security

Deep fake Detection is the task of detecting the fake images that have been generated using deep learning techniques. Deep fakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deep fake detection is to identify such manipulations and distinguish them from real videos or images. Deep fake technology has emerged as a significant concern in recent years, presenting challenges in various fields, including media authenticity, privacy, and security.

Deepfake (DF) involves utilizing artificial intelligence (AI) technology to synthesize or manipulate images, voices, and other human or object data. However, recent times have seen a surge in instances of DF technology misuse, raising concerns about cybercrime and the credibility of manipulated information. The objective of this study is to devise a method that employs remote photoplethysmography (rPPG) biosignals for DF detection. The face was divided into five regions based on landmarks, with automatic extraction performed on the neck region. We conducted rPPG signal extraction from each facial area and the neck region was defined as the ground truth. The five signals extracted from the face were used as inputs to an support vector machine (SVM) model by calculating the euclidean distance between each signal and the signal extracted from the neck region, measuring rPPG signal similarity with five features. Our approach demonstrated robust performance with an area under the curve (AUC) score of 91.2% on the audio‐driven dataset and 99.7% on the face swapping generative adversarial network (FSGAN) dataset, even though we only used datasets excluding DF techniques that can be visually identified in Korean DF Detection Dataset (KoDF). Therefore, our research findings demonstrate that similarity features of rPPG signals can be utilized as key features for detecting DFs.

This study examines the dynamics of disinformation spread on X (formerly Twitter) in the context of Türkiye’s Letter Revolution, employing the Hypodermic Needle Model and Cultivation Theory as primary theoretical frameworks. By applying these theories to social media disinformation, we offer a novel approach to understanding both the immediate impact and long-term effects of false narratives on public perception. Our research analyzes a unique dataset of 36.9 million tweets from accounts closed due to disinformation spread, focusing on the #harfdevrimi hashtag. The purpose of this paper is to demonstrate how these communication theories can be effectively applied to modern digital environments, providing insights into the mechanisms of disinformation dissemination and its role in societal polarization. A comprehensive theoretical framework for analyzing social media disinformation, methodological innovations in large-scale social media data analysis, and critical insights into the challenges of maintaining an informed citizenry in politically sensitive contexts will be presented. This research contributes to the growing body of literature on media manipulation and offers a model for future studies on disinformation in digital spaces.

Due to the abuse of deep forgery technology, the research on forgery detection methods has become increasingly urgent. The corresponding relationship between the frequency spectrum information and the spatial clues, which is often neglected by current methods, could be conducive to a more accurate and generalized forgery detection. Motivated by this inspiration, we propose a wavelet‐based texture mining and enhancement framework for face forgery detection. First, we introduce a frequency‐guided texture enhancement (FGTE) module that mining the high‐frequency information to improve the network’s extraction of effective texture features. Next, we propose a global–local feature refinement (GLFR) module to enhance the model’s leverage of both global semantic features and local texture features. Moreover, the interactive fusion module (IFM) is designed to fully incorporate the enhanced texture clues with spatial features. The proposed method has been extensively evaluated on five public datasets, such as FaceForensics++ (FF++), deepfake (DF) detection (DFD) challenge (DFDC), Celeb‐DFv2, DFDC preview (DFDC‐P), and DFD, for face forgery detection, yielding promising performance within and cross dataset experiments.

This comprehensive study investigates the pervasive issue of deep fakes within the context of deep learning applications, focusing on their detection and production. Utilizing a diverse array of deep learning algorithms, including InceptionResnetV2, VGG19, CNN, Xception, InceptionV3, EfficientNetB1, DenseNet121, Hybrid Model, LSTM, ResNext-LSTM, and MRI-GAN, the research systematically evaluates their effectiveness in detecting deep fakes. Results reveal varying levels of accuracy, with Xception emerging as the most precise algorithm, achieving an accuracy of 99.32%. Notably, InceptionResnetV2 and DenseNet121 also demonstrate robust performance, with accuracies surpassing 99%. However, certain models like VGG19 and LSTM exhibit lower accuracy rates, underscoring the need for further refinement. These findings underscore the urgent necessity for robust detection mechanisms amidst the proliferation of malicious deep fakes, safeguarding against potential societal ramifications such as misinformation and privacy breaches.

The Approach of European International Organizations to the Regulation of the Impact of Artificial Intelligence on Electoral Processes The dynamic development of technologies known as artificial intelligence (AI) raises challenges and concerns about its applications in social life, including negative effects on the accuracy of democratic processes. Risks, related to manipulation and the spread of disinformation or propaganda on online platforms, may – with the use of AI – multiply. In electoral processes, the creation of false images of reality or the use of bots to control the electoral agenda, threaten to disrupt communications and even uncertain election results, and ultimately destroy democracy. However, properly designed AI tools can also improve democratic processes, more deeply engaging people as potential voters. Pan-European organizations, have pointed out the need to establish international standards for the application of AI, so that this technology is not tucked away in a “black box of algorithms,” which should be transparent, understandable and controlled according to established principles of accountability. Dynamiczny rozwój technologii określanych jako sztuczna inteligencja (SI) rodzi wyzwania i obawy dotyczące jej zastosowań w życiu społecznych, w tym negatywnych skutków dla prawidłowości procesów demokratycznych. Znane już zagrożenia, związane z manipulacjami i rozprzestrzenianiem dezinformacji czy propagandy w komunikacji na platformach internetowych, mogą – przy pomocy SI – ulec zwielokrotnieniu. W procesach wyborczych tworzenie fałszywych obrazów rzeczywistości czy wykorzystanie botów do sterowania agendą wyborczą, grożą zakłócaniem komunikacji, a nawet niepewnymi wynikami wyborów, a w konsekwencji destrukcją demokracji. Jednakże odpowiednio zaprojektowane narzędzia SI mogą także demokratyczne procesy usprawniać, głębiej angażując ludzi jako potencjalnych i świadomych wyborców. Organizacje międzynarodowe o zasięgu paneuropejskim, takie jak Rada Europy, UE czy OBWE, wskazywały na konieczność ustanowienia międzynarodowych standardów zastosowanie SI, aby tej technologii nie schowano w „czarnej skrzynce algorytmów”, które powinny być transparentne, zrozumiałe i kontrolowane zgodnie z ustalonymi zasadami odpowiedzialności.

Relevance. The need to study media literacy is driven by the growing destructive influence of information, which is transforming public consciousness and increasing the risk of manipulation in the digital environment. Declining trust in the media, the spread of disinformation, and the growing role of social networks as the main source of news require an in-depth analysis of new communication threats and the search for effective means to overcome them, especially in the field of education. Therefore, the problem of critical thinking among young people, who are at the epicenter of digital interaction and most susceptible to information influence, is becoming increasingly relevant. Aim. The purpose of this scientific article is to empirically measure the levels of media literacy and critical thinking in a social sample, as well as to identify the statistically significant impact of variables such as age, level of education, and participation in media education programs on the formation of relevant cognitive competencies. Methods. During the study, a sociological survey was conducted among Ukrainian citizens in 2024-2025 using a structured online questionnaire, which allowed us to form the initial data for further analysis. Based on this data, the hypotheses were tested using the classic Student's t-test for independent samples with the JASP software package (in particular, the “Descriptives” and “Independent Samples T-Test” tools). Results. The results of the analysis revealed that taking media education courses has a statistically significant impact on the level of media literacy (M = 32.915 and 20.910) and critical thinking (M = 33.111 and 21.059) among respondents, as evidenced by a t-test with a high Cohen's effect (d ≈ -1.57, p < 0.001). The educational level of respondents has a significant impact on cognitive indicators, in particular, respondents with higher education had significantly higher mean values of media literacy (M = 30.023 and 18.473 at p < 0.001) and critical thinking (M = 30.556 and 18.871 at p < 0.001). In addition, a statistically significant age differentiation was found in the levels of media literacy (Group 0: M = 29.904, SD = 6.301; Group 1: M = 20.448, SD = 9.380) and critical thinking (Group 0: M = 30.201, SD = 6.530; Group 1: M = 20.510; SD = 9.209), as evidenced by high t-statistics (t = -13.244; p < .001) and a large Cohen's effect (d = -1.187), indicating a significant association of younger age with higher levels of cognitive competence. Conclusions. The study revealed a low level of media literacy and critical thinking among certain groups of respondents, which indicates the need to implement systematic educational programs aimed at developing cognitive and socio-communication skills. Importantly, the findings highlight the potential of e-learning as an effective environment for media literacy development, since online courses and digital learning platforms can flexibly adapt content for different age and educational groups. This supports the practical integration of media literacy modules into formal and informal e-learning formats, demonstrating how digital tools enhance accessibility and personalization of training. Furthermore, the results reveal a shift in e-learning theory, showing that the effectiveness of online learning in this domain depends not only on access to information but also on the design of interactive, competence-oriented educational interventions. Thus, the study contributes to both the practice and theory of e-learning by identifying critical factors for structuring digital courses that strengthen resilience against disinformation and manipulation. At the same time, the lack of a deeper analysis of socio-cultural factors is a limitation, which opens prospects for further interdisciplinary research in this area.

Deepfake (DF) detectors face significant challenges when deployed in real-world environments, particularly when encountering test samples deviated from training data through either postprocessing manipulations or distribution shifts. We demonstrate postprocessing techniques can completely obscure generation artifacts presented in DF samples, leading to performance degradation of DF detectors. To address these challenges, we propose Think Twice before Adaptation (T2A), a novel online test-time adaptation method that enhances the adaptability of detectors during inference without requiring access to source training data or labels. Our key idea is to enable the model to explore alternative options through an Uncertainty-aware Negative Learning objective rather than solely relying on its initial predictions as commonly seen in entropy minimization (EM)-based approaches. We also introduce an Uncertain Sample Prioritization strategy and Gradients Masking technique to improve the adaptation by focusing on important samples and model parameters. Our theoretical analysis demonstrates that the proposed negative learning objective exhibits complementary behavior to EM, facilitating better adaptation capability. Empirically, our method achieves state-of-the-art results compared to existing test-time adaptation (TTA) approaches and significantly enhances the resilience and generalization of DF detectors during inference.

Election Meddling, the 'Unnoticed' Dissonance between 'Alarming Warnings' and the Reductive Assessments about Their 'Effects' on Returns and the Not-Necessarily 'Foreign' Media Manipulation