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Artykuły w czasopismach na temat „DETECTING DEEPFAKES”

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Data publikacji: 11 września 2023

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1

Mai, Kimberly T., Sergi Bray, Toby Davies, and Lewis D. Griffin. "Warning: Humans cannot reliably detect speech deepfakes." PLOS ONE 18, no. 8 (2023): e0285333. http://dx.doi.org/10.1371/journal.pone.0285333.

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Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unre
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Dobber, Tom, Nadia Metoui, Damian Trilling, Natali Helberger, and Claes de Vreese. "Do (Microtargeted) Deepfakes Have Real Effects on Political Attitudes?" International Journal of Press/Politics 26, no. 1 (2020): 69–91. http://dx.doi.org/10.1177/1940161220944364.

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Deepfakes are perceived as a powerful form of disinformation. Although many studies have focused on detecting deepfakes, few have measured their effects on political attitudes, and none have studied microtargeting techniques as an amplifier. We argue that microtargeting techniques can amplify the effects of deepfakes, by enabling malicious political actors to tailor deepfakes to susceptibilities of the receiver. In this study, we have constructed a political deepfake (video and audio), and study its effects on political attitudes in an online experiment ( N = 278). We find that attitudes towar
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3

Vinogradova, Ekaterina. "The malicious use of political deepfakes and attempts to neutralize them in Latin America." Latinskaia Amerika, no. 5 (2023): 35. http://dx.doi.org/10.31857/s0044748x0025404-3.

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Deepfake technology has revolutionized the field of artificial intelligence and communication processes, creating a real threat of misinformation of target audiences on digital platforms. The malicious use of political deepfakes has become widespread between 2017 and 2023. The political leaders of Argentina, Brazil, Colombia and Mexico were attacked with elements of doxing. Fake videos that used the politicians' faces undermined their reputations, diminishing the trust of the electorate, and became an advanced tool for manipulating public opinion. A series of political deepfakes has r
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Singh, Preeti, Khyati Chaudhary, Gopal Chaudhary, Manju Khari, and Bharat Rawal. "A Machine Learning Approach to Detecting Deepfake Videos: An Investigation of Feature Extraction Techniques." Journal of Cybersecurity and Information Management 9, no. 2 (2022): 42–50. http://dx.doi.org/10.54216/jcim.090204.

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Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our
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Das, Rashmiranjan, Gaurav Negi, and Alan F. Smeaton. "Detecting Deepfake Videos Using Euler Video Magnification." Electronic Imaging 2021, no. 4 (2021): 272–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-272.

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Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could ea
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6

Raza, Ali, Kashif Munir, and Mubarak Almutairi. "A Novel Deep Learning Approach for Deepfake Image Detection." Applied Sciences 12, no. 19 (2022): 9820. http://dx.doi.org/10.3390/app12199820.

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Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Ki
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7

Jameel, Wildan J., Suhad M. Kadhem, and Ayad R. Abbas. "Detecting Deepfakes with Deep Learning and Gabor Filters." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (2022): 18–22. http://dx.doi.org/10.14500/aro.10917.

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The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of inp
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Giudice, Oliver, Luca Guarnera, and Sebastiano Battiato. "Fighting Deepfakes by Detecting GAN DCT Anomalies." Journal of Imaging 7, no. 8 (2021): 128. http://dx.doi.org/10.3390/jimaging7080128.

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To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this
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9

Lim, Suk-Young, Dong-Kyu Chae, and Sang-Chul Lee. "Detecting Deepfake Voice Using Explainable Deep Learning Techniques." Applied Sciences 12, no. 8 (2022): 3926. http://dx.doi.org/10.3390/app12083926.

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Fake media, generated by methods such as deepfakes, have become indistinguishable from real media, but their detection has not improved at the same pace. Furthermore, the absence of interpretability on deepfake detection models makes their reliability questionable. In this paper, we present a human perception level of interpretability for deepfake audio detection. Based on their characteristics, we implement several explainable artificial intelligence (XAI) methods used for image classification on an audio-related task. In addition, by examining the human cognitive process of XAI on image clas
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10

Gadgilwar, Jitesh, Kunal Rahangdale, Om Jaiswal, Parag Asare, Pratik Adekar, and Prof Leela Bitla. "Exploring Deepfakes - Creation Techniques, Detection Strategies, and Emerging Challenges: A Survey." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1491–95. http://dx.doi.org/10.22214/ijraset.2023.49681.

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Abstract: Deep learning, integrated with Artificial Intelligence algorithms, has brought about numerous beneficial practical technologies. However, it also brings up a problem that the world is facing today. Despite its innumerable suitable applications, it poses a danger to public personal privacy, democracy, and corporate credibility. One such use that has emerged is deepfake, which has caused chaos on the internet. Deepfake manipulates an individual's image and video, creating problems in differentiating the original from the fake. This requires a solution in today's period to counter and a
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11

Dobrobaba, M. B. "Deepfakes as a Threat to Human Rights." Lex Russica 75, no. 11 (2022): 112–19. http://dx.doi.org/10.17803/1729-5920.2022.192.11.112-119.

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The paper is devoted to such a new threat to human rights arising amid digitalization as deepfake technologies. The author shows that the use of such technologies is a tool that can have both positive and negative effects. In particular, the use of dipfakes entails a threat to privacy, violations of the honor and dignity of citizens. In this regard, the legislator is faced with the task of developing and implementing a set of measures, the application of which will minimize the possibility of violation of citizens’ rights by deepfake technologies. It is proposed to direct the efforts of the st
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12

Salvi, Davide, Honggu Liu, Sara Mandelli, et al. "A Robust Approach to Multimodal Deepfake Detection." Journal of Imaging 9, no. 6 (2023): 122. http://dx.doi.org/10.3390/jimaging9060122.

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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
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13

Tursman, Eleanor. "Detecting deepfakes using crowd consensus." XRDS: Crossroads, The ACM Magazine for Students 27, no. 1 (2020): 22–25. http://dx.doi.org/10.1145/3416061.

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14

Mateen, Marium, and Narmeen Zakaria Bawany. "Deep Learning Approach for Detecting Audio Deepfakes in Urdu." NUML International Journal of Engineering and Computing 2, no. 1 (2023): 1–11. http://dx.doi.org/10.52015/nijec.v2i1.37.

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The application of Deep Learning algorithms for speech synthesis has led to the widespread generation of Audio Deepfakes, which are becoming a real threat to voice interfaces. Audio Deepfakes are fake audio recordings that are difficult to differentiate from real recordings because they use AI-generated techniques to clone human voices. When prominent speakers, celebrities, and politicians are the target of Audio Deepfakes, this technology can potentially undermine public confidence and trustworthiness. Therefore, it is essential to create efficient methods and technologies to identify and sto
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15

Choi, Nakhoon, and Heeyoul Kim. "DDS: Deepfake Detection System through Collective Intelligence and Deep-Learning Model in Blockchain Environment." Applied Sciences 13, no. 4 (2023): 2122. http://dx.doi.org/10.3390/app13042122.

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With the spread of mobile devices and the improvement of the mobile service environment, the use of various Internet content providers (ICPs), including content services such as YouTube and video hosting services, has increased significantly. Video content shared in ICP is used for information delivery and issue checking based on accessibility. However, if the content registered and shared in ICP is manipulated through deepfakes and maliciously distributed to cause political attacks or social problems, it can cause a very large negative effect. This study aims to propose a deepfake detection s
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16

Wan, Da, Manchun Cai, Shufan Peng, Wenkai Qin, and Lanting Li. "Deepfake Detection Algorithm Based on Dual-Branch Data Augmentation and Modified Attention Mechanism." Applied Sciences 13, no. 14 (2023): 8313. http://dx.doi.org/10.3390/app13148313.

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Mainstream deepfake detection algorithms generally fail to fully extract forgery traces and have low accuracy when detecting forged images with natural corruptions or human damage. On this basis, a new algorithm based on an adversarial dual-branch data augmentation framework and a modified attention mechanism is proposed in this paper to improve the robustness of detection models. First, this paper combines the traditional random sampling augmentation method with the adversarial sample idea to enhance and expand the forged images in data preprocessing. Then, we obtain training samples with div
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17

Frick, Raphael Antonius, Sascha Zmudzinski, and Martin Steinebach. "Detecting Deepfakes with Haralick’s Texture Properties." Electronic Imaging 2021, no. 4 (2021): 271–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-271.

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In the recent years, the detection of deepfake videos has become a major topic in the field of digital media forensics, as the amount of such videos circulating on the internet has drastically risen. Providers of content, such as Facebook and Amazon, have become aware of this new threat to spreading misinformation on the Internet. In this work, a novel forgery detection method based on the texture analysis known from image classification and segmentation is proposed. In the experimental results, its performance has shown to be comparable to related works.
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18

Taeb, Maryam, and Hongmei Chi. "Comparison of Deepfake Detection Techniques through Deep Learning." Journal of Cybersecurity and Privacy 2, no. 1 (2022): 89–106. http://dx.doi.org/10.3390/jcp2010007.

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Deepfakes are realistic-looking fake media generated by deep-learning algorithms that iterate through large datasets until they have learned how to solve the given problem (i.e., swap faces or objects in video and digital content). The massive generation of such content and modification technologies is rapidly affecting the quality of public discourse and the safeguarding of human rights. Deepfakes are being widely used as a malicious source of misinformation in court that seek to sway a court’s decision. Because digital evidence is critical to the outcome of many legal cases, detecting deepfa
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19

Amatika, Faith. "The Regulation of Deepfakes in Kenya." Journal of Intellectual Property and Information Technology Law (JIPIT) 2, no. 1 (2022): 145–86. http://dx.doi.org/10.52907/jipit.v2i1.208.

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‘Truth has become elusive.’ ‘We are entering into an age of information apocalypse.’ ‘Seeing is no longer believing unless you saw it live.’ These and similar statements characterise most discussions in the present highly digital age. With the borderless nature of the Internet, it is possible to share videos, photos, and information with countless people provided one has a reliable internet source and a smart gadget, for instance, a mobile phone. Technological advancements have also made it possible for tech-savvy individuals to compile computer programs that make it possible to swap faces and
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20

Arshed, Muhammad Asad, Ayed Alwadain, Rao Faizan Ali, Shahzad Mumtaz, Muhammad Ibrahim, and Amgad Muneer. "Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network." Mathematics 11, no. 17 (2023): 3710. http://dx.doi.org/10.3390/math11173710.

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With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the
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21

Yasrab, Robail, Wanqi Jiang, and Adnan Riaz. "Fighting Deepfakes Using Body Language Analysis." Forecasting 3, no. 2 (2021): 303–21. http://dx.doi.org/10.3390/forecast3020020.

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Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepf
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Tran, Van-Nhan, Suk-Hwan Lee, Hoanh-Su Le, and Ki-Ryong Kwon. "High Performance DeepFake Video Detection on CNN-Based with Attention Target-Specific Regions and Manual Distillation Extraction." Applied Sciences 11, no. 16 (2021): 7678. http://dx.doi.org/10.3390/app11167678.

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The rapid development of deep learning models that can produce and synthesize hyper-realistic videos are known as DeepFakes. Moreover, the growth of forgery data has prompted concerns about malevolent intent usage. Detecting forgery videos are a crucial subject in the field of digital media. Nowadays, most models are based on deep learning neural networks and vision transformer, SOTA model with EfficientNetB7 backbone. However, due to the usage of excessively large backbones, these models have the intrinsic drawback of being too heavy. In our research, a high performance DeepFake detection mod
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Le, Vincent. "The Deepfakes to Come: A Turing Cop’s Nightmare." Identities: Journal for Politics, Gender and Culture 17, no. 2-3 (2020): 8–18. http://dx.doi.org/10.51151/identities.v17i2-3.468.

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In 1950, Turing proposed to answer the question “can machines think” by staging an “imitation game” where a hidden computer attempts to mislead a human interrogator into believing it is human. While the cybercrime of bots defrauding people by posing as Nigerian princes and lascivious e-girls indicates humans have been losing the Turing test for some time, this paper focuses on “deepfakes,” artificial neural nets generating realistic audio-visual simulations of public figures, as a variation on the imitation game. Deepfakes blur the lines between fact and fiction, making it possible for the mer
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Frick, Raphael Antonius, Sascha Zmudzinski, and Martin Steinebach. "Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts." Electronic Imaging 2020, no. 4 (2020): 116–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.4.mwsf-116.

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In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.
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Saxena, Akash, Dharmendra Yadav, Manish Gupta, et al. "Detecting Deepfakes: A Novel Framework Employing XceptionNet-Based Convolutional Neural Networks." Traitement du Signal 40, no. 3 (2023): 835–46. http://dx.doi.org/10.18280/ts.400301.

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A. Abu-Ein, Ashraf, Obaida M. Al-Hazaimeh, Alaa M. Dawood, and Andraws I. Swidan. "Analysis of the current state of deepfake techniques-creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1659-1667.

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algo
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Godulla, Alexander, Christian P. Hoffmann, and Daniel Seibert. "Dealing with deepfakes – an interdisciplinary examination of the state of research and implications for communication studies." Studies in Communication and Media 10, no. 1 (2021): 72–96. http://dx.doi.org/10.5771/2192-4007-2021-1-72.

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Using artificial intelligence, it is becoming increasingly easy to create highly realistic but fake video content - so-called deepfakes. As a result, it is no longer possible always to distinguish real from mechanically created recordings with the naked eye. Despite the novelty of this phenomenon, regulators and industry players have started to address the risks associated with deepfakes. Yet research on deepfakes is still in its infancy. This paper presents findings from a systematic review of English-language deepfake research to identify salient discussions. We find that, to date, deepfake
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Shahzad, Hina Fatima, Furqan Rustam, Emmanuel Soriano Flores, Juan Luís Vidal Mazón, Isabel de la Torre Diez, and Imran Ashraf. "A Review of Image Processing Techniques for Deepfakes." Sensors 22, no. 12 (2022): 4556. http://dx.doi.org/10.3390/s22124556.

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complement
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Vinay, A., Paras S. Khurana, T. B. Sudarshan, et al. "AFMB-Net." Tehnički glasnik 16, no. 4 (2022): 503–8. http://dx.doi.org/10.31803/tg-20220403080215.

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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 deep
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Jiang, Jianguo, Boquan Li, Baole Wei, et al. "FakeFilter: A cross-distribution Deepfake detection system with domain adaptation." Journal of Computer Security 29, no. 4 (2021): 403–21. http://dx.doi.org/10.3233/jcs-200124.

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Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some
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Guarnera, Luca, Oliver Giudice, Francesco Guarnera, et al. "The Face Deepfake Detection Challenge." Journal of Imaging 8, no. 10 (2022): 263. http://dx.doi.org/10.3390/jimaging8100263.

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Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable o
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32

López-Gil, Juan-Miguel, Rosa Gil, and Roberto García. "Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression." Computational Intelligence and Neuroscience 2022 (October 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/1332122.

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Recent technological advancements in Artificial Intelligence make it easy to create deepfakes and hyper-realistic videos, in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake datab
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Khormali, Aminollah, and Jiann-Shiun Yuan. "ADD: Attention-Based DeepFake Detection Approach." Big Data and Cognitive Computing 5, no. 4 (2021): 49. http://dx.doi.org/10.3390/bdcc5040049.

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Recent advancements of Generative Adversarial Networks (GANs) pose emerging yet serious privacy risks threatening digital media’s integrity and trustworthiness, specifically digital video, through synthesizing hyper-realistic images and videos, i.e., DeepFakes. The need for ascertaining the trustworthiness of digital media calls for automatic yet accurate DeepFake detection algorithms. This paper presents an attention-based DeepFake detection (ADD) method that exploits the fine-grained and spatial locality attributes of artificially synthesized videos for enhanced detection. ADD framework is c
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Shad, Hasin Shahed, Md Mashfiq Rizvee, Nishat Tasnim Roza, et al. "Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (December 16, 2021): 1–18. http://dx.doi.org/10.1155/2021/3111676.

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Generation Z is a data-driven generation. Everyone has the entirety of humanity’s knowledge in their hands. The technological possibilities are endless. However, we use and misuse this blessing to face swap using deepfake. Deepfake is an emerging subdomain of artificial intelligence technology in which one person’s face is overlaid over another person’s face, which is very prominent across social media. Machine learning is the main element of deepfakes, and it has allowed deepfake images and videos to be generated considerably faster and at a lower cost. Despite the negative connotations assoc
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Noreen, Iram, Muhammad Shahid Muneer, and Saira Gillani. "Deepfake attack prevention using steganography GANs." PeerJ Computer Science 8 (October 20, 2022): e1125. http://dx.doi.org/10.7717/peerj-cs.1125.

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Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techn
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Bogdanova, D. A. "About some aspects of digital ecology." Informatics in school, no. 7 (November 19, 2021): 15–19. http://dx.doi.org/10.32517/2221-1993-2021-20-7-15-19.

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The situation with the spread of disinformation in the modern information environment and the complexity of the presentation and perception of refutations caused by the effects of "lasting influence", "reverse action" and the existence of polarized communities — "echo chambers" have been analysed. The dangers of deepfakes have been considered. A new type of content marketing with the self-explanatory name clickbait has been considered. It has seriously revolutionized the way content is distributed and attracted readers' attention. The proficiency of media literacy skills by children and adults
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Akhtar, Zahid. "Deepfakes Generation and Detection: A Short Survey." Journal of Imaging 9, no. 1 (2023): 18. http://dx.doi.org/10.3390/jimaging9010018.

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Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniq
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Coccomini, Davide Alessandro, Roberto Caldelli, Fabrizio Falchi, and Claudio Gennaro. "On the Generalization of Deep Learning Models in Video Deepfake Detection." Journal of Imaging 9, no. 5 (2023): 89. http://dx.doi.org/10.3390/jimaging9050089.

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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
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Olariu, Oana. "Critical Thinking as Dynamic Shield against Media Deception. Exploring Connections between the Analytical Mind and Detecting Disinformation Techniques and Logical Fallacies in Journalistic Production." Logos Universality Mentality Education Novelty: Social Sciences 11, no. 1 (2022): 29–57. http://dx.doi.org/10.18662/lumenss/11.1/61.

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As research on fake news and deepfakes advanced, a growing consensus is building towards considering critical and analytical thinking, as well as general or topic specific knowledge, which is related to information literacy, as the main significant or effective factors in curving vulnerability to bogus digital content. However, although the connection might be intuitive, the processes linking critical or analytical thinking to manipulation resistance are still not known and understudied. The present study aims to contribute to filling this gap by exploring how analytically driven conclusions o
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Maharjan, Ashish, and Asish Shakya. "Learning Approaches used by Different Applications to Achieve Deep Fake Technology." Interdisciplinary Journal of Innovation in Nepalese Academia 2, no. 1 (2023): 96–101. http://dx.doi.org/10.3126/idjina.v2i1.55969.

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Deepfake technology is an emerging field that has gained considerable attention in recent years. Deepfakes are synthetic media, including images, videos, and audio recordings, that are manipulated by advanced machine learning algorithms to produce convincing yet entirely artificial content. This paper explores the various applications and the technologies used by them to achieve deep fake. The machine learning algorithms and the software are used by each of them for proper execution of the technology. Further, we discuss the future prospects of the deepfake technology and explore future direct
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Lee, Gihun, and Mihui Kim. "Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision." Sensors 21, no. 21 (2021): 7367. http://dx.doi.org/10.3390/s21217367.

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Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various problems, such as security, privacy, and ethics, also occur owing to the development of artificial intelligence. One such problem are deepfakes. Deepfake is a compound word for deep learning and fake. It refers to a fake video created using artificial intelligence technology or the production process itself. Deepfakes can be exploited for political abuse, pornography, and fake information. This paper proposes a method to determine integrity by analyzin
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Kale, Prachi. "Forensic Verification and Detection of Fake Video using Deep Fake Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2789–94. http://dx.doi.org/10.22214/ijraset.2021.35599.

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In the course of the most recen years, the ascent in cell phones and interpersonal organizations has made computerized pictures and recordings basic advanced articles. per reports, right around two billion pictures are transferred every day on the web. This gigantic utilization of computerized pictures has been trailed by an increment of methods to change picture substance, utilizing altering programming like Photoshop for instance. Counterfeit recordings and pictures made by deepFake methods turned into a decent open issue as of late. These days a few procedures for facial control in recordin
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Khormali, Aminollah, and Jiann-Shiun Yuan. "DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer." Applied Sciences 12, no. 6 (2022): 2953. http://dx.doi.org/10.3390/app12062953.

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The ever-growing threat of deepfakes and large-scale societal implications has propelled the development of deepfake forensics to ascertain the trustworthiness of digital media. A common theme of existing detection methods is using Convolutional Neural Networks (CNNs) as a backbone. While CNNs have demonstrated decent performance on learning local discriminative information, they fail to learn relative spatial features and lose important information due to constrained receptive fields. Motivated by the aforementioned challenges, this work presents DFDT, an end-to-end deepfake detection framewo
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Mehra, Aman, Akshay Agarwal, Mayank Vatsa, and Richa Singh. "Detection of Digital Manipulation in Facial Images (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15845–46. http://dx.doi.org/10.1609/aaai.v35i18.17919.

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Advances in deep learning have enabled the creation of photo-realistic DeepFakes by switching the identity or expression of individuals. Such technology in the wrong hands can seed chaos through blackmail, extortion, and forging false statements of influential individuals. This work proposes a novel approach to detect forged videos by magnifying their temporal inconsistencies. A study is also conducted to understand role of ethnicity bias due to skewed datasets on deepfake detection. A new dataset comprising forged videos of Indian ethnicity individuals is presented to facilitate this study.
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Yavuzkilic, Semih, Abdulkadir Sengur, Zahid Akhtar, and Kamran Siddique. "Spotting Deepfakes and Face Manipulations by Fusing Features from Multi-Stream CNNs Models." Symmetry 13, no. 8 (2021): 1352. http://dx.doi.org/10.3390/sym13081352.

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Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of another person’s face using artificial neural networks. Deepfake manipulations may be done with a variety of techniques and applications. A quintessential countermeasure against deepfake or face manipulation is deepfake detection method. Most of the existing detection methods perform well under symmetric data distributions, but are still not robust to asymmetric datasets variations and novel deepfake/manipulation types. In
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Balasubramanian, Saravana Balaji, Jagadeesh Kannan R, Prabu P, Venkatachalam K, and Pavel Trojovský. "Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection." PeerJ Computer Science 8 (July 13, 2022): e1040. http://dx.doi.org/10.7717/peerj-cs.1040.

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In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of t
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Yang, Sung-Hyun, Keshav Thapa, and Barsha Lamichhane. "Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets." Sensors 22, no. 23 (2022): 9121. http://dx.doi.org/10.3390/s22239121.

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The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and samp
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Amoah-Yeboah, Yaw. "Biometric Spoofing and Deepfake Detection." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (2022): 279–84. http://dx.doi.org/10.22624/aims/crp-bk3-p45.

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Biometrics have increasingly become most suited mechanisms for identification and authentication in the use of diverse technologies and systems. However, much they prove to be more robust than other identification and authentication mechanisms, there is also an upsurge with privacy and security concerns. With AI being at the forefront of our technological advancement, it has been to our advantage and also, somehow to our detriment. People are constantly deriving ways to either trick biometric sensors to crack and bypass these authentication protocols. The practice of these nefarious activities
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Đorđević, Miljan, Milan Milivojević, and Ana Gavrovska. "DeepFake video production and SIFT-based analysis." Telfor Journal 12, no. 1 (2020): 22–27. http://dx.doi.org/10.5937/telfor2001022q.

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Nowadays advantages in face-based modification using DeepFake algorithms made it possible to replace a face of one person with a face of another person. Thus, it is possible to make not only copy-move modifications, but to implement artificial intelligence and deep learning for replacing face movements from one person to another. Still images can be converted into video sequences. Consequently, the contemporaries, historical figures or even animated characters can be lively presented. Deepfakes are becoming more and more successful and it is difficult to detect them in some cases. In this pape
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Binh, Le Minh, and Simon Woo. "ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 122–30. http://dx.doi.org/10.1609/aaai.v36i1.19886.

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Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capabil
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