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Papers on “deepfake detection synthetic media”

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1. Deepfakes and beyond: A Survey of face manipulation and fake detection

   Rubén Tolosana, Rubén Vera-Rodríguez, Julián Fiérrez, et al. · 2020 · Hispana · 1,191 cites

2. Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News

   Cristian Vaccari, Andrew Chadwick · 2020 · Social Media + Society · 940 cites

   Artificial Intelligence (AI) now enables the mass creation of what have become known as “deepfakes”: synthetic videos that closely resemble real videos. Integrating theories about the power of visual communication and the role played by uncertainty in undermining trust in public discourse, we explain the likely contribution of deepfakes to online disinformation. Administering novel experimental treatments to a large representative sample of the United Kingdom population allowed us to compare people’s evaluations of deepfakes. We find that people are more likely to feel uncertain than to be misled by deepfakes, but this resulting uncertainty, in turn, reduces trust in news on social media. We

3. Deepfake Detection: A Systematic Literature Review

   Md. Shohel Rana, Mohammad Nur Nobi, Beddhu Murali, et al. · 2022 · IEEE Access · 563 cites

   Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in th

4. DeepFakes: a New Threat to Face Recognition? Assessment and Detection

   Pavel Korshunov, Sébastien Marcel · 2018 · arXiv (Cornell University) · 497 cites

   It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact

5. Deep learning for deepfakes creation and detection: A survey

   Thanh Thi Nguyen, Thanh Thi Nguyen, Quoc Viet Hung Nguyen, et al. · 2022 · Computer Vision and Image Understanding · 447 cites

6. Deepfake Video Detection through Optical Flow Based CNN

   Irene Amerini, Leonardo Galteri, Roberto Caldelli, et al. · 2019 · 409 cites

   Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video fram

7. Deepfake detection using deep learning methods: A systematic and comprehensive review

   Arash Heidari, Nima Jafari Navimipour, Hasan Dağ, et al. · 2023 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 335 cites

   Abstract Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to

8. DeepFake Detection for Human Face Images and Videos: A Survey

   Asad Malik, Minoru Kuribayashi, Sani M. Abdullahi, et al. · 2022 · IEEE Access · 287 cites

   Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural net