18 papers · ranked by Valyu relevance
Oliver Giudice, Luca Guarnera, Sebastiano Battiato, Irene Amerini + 2 more
'Gianmarco Baldini' 'Francesco Leotta'] 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…
Luca Guarnera, Oliver Giudice, Francesco Guarnera, Alessandro Ortis + 17 more
'Giovanni Puglisi' 'Antonino Paratore' 'Linh M. Q. Bui' 'Marco Fontani' 'Davide Alessandro Coccomini' 'Roberto Caldelli' 'Fabrizio Falchi' 'Claudio Gennaro' 'Nicola Messina' 'Giuseppe Amato' 'Gianpaolo Perelli' 'Sara Concas' 'Carlo Cuccu' 'Giulia Orrù' 'Gian Luca Marcialis' 'Sebastiano Battiato' 'Mohamed Daoudi']…
Gihun Lee, Mihui Kim, Yun Zhang, KWONG Tak Wu Sam + 2 more
'Tiesong Zhao'] 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…
Didem Pehlivanoglu, Mengdi Zhu, Jialong Zhen, Aude A. Gagnon-Roberge + 4 more
Deepfakes are synthetic media created by deep-generative methods to fake a person’s audio-visual representation. Growing sophistication of deepfake technology poses significant challenges for both machine learning (ML) algorithms and humans. Here we used real and deepfake static face images (Study 1) and dynamic videos…
Hina Fatima Shahzad, Furqan Rustam, Emmanuel Soriano Flores, Juan Luís Vidal Mazón + 5 more
'Juan Luís Vidal Mazón' 'Isabel de la Torre Diez' 'Imran Ashraf' 'Zhaoyang Wang' 'Minh P. Vo' 'Hieu Nguyen'] 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…
Sonam Singh, Amol Dhumane
Title: Highlights 1. • Presents an in-depth review of deepfake generation and detection, highlighting AI methods such as GANs, face synthesis, and speech cloning. 2. • Evaluates critically the weaknesses of biometric systems and the difficulties of cross-dataset testing for deepfake detection. 3. • Suggest…
Trung-Nghia Le, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also…
Yuanqing Ding, Fanliang Bu, Hanming Zhai, Zhiwen Hou + 2 more
'Jiachen Yang'] The malicious use of deepfake videos seriously affects information security and brings great harm to society. Currently, deepfake videos are mainly generated based on deep learning methods, which are difficult to be recognized by the naked eye, therefore, it is of great significance to study accurate…
Luca Guarnera, Oliver Giudice, Sebastiano Battiato
The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on adhoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new…
Will Rowan, Nick Pears
—Deepfakes are a form of synthetic image generation used to generate fake videos of individuals for malicious purposes. The resulting videos may be used to spread misinformation, reduce trust in media, or as a form of blackmail. These threats necessitate automated methods of deepfake video detection. This paper…
Kundan Patil, Shrushti Kale, Jaivanti Dhokey, Abhishek A. Gulhane
A deep learning-based technique called deepfake has made it easier to change or modify images and videos. In investigations and court, visual evidence is commonly employed. These pieces of evidence may now be a suspect due to technological advancements, particularly Deepfake. Photographs and movies that have been…
Kirill Vyshegorodtsev, Dmitry Kudiyarov, Alexander Balashov, A. Kuzmin
geometric-fakeness features Authors: ['Kirill Vyshegorodtsev' 'Dmitry Kudiyarov' 'Alexander Balashov' 'A. Kuzmin'] Abstract—Due to the development of facial manipulation techniques in recent years deepfake detection in video stream became an important problem for face biometrics, brand monitoring or online video…
Lixia Ma, Puning Yang, Yuting Xu, Ziming Yang + 2 more
- Wepresent a comprehensive review ofthe mostrecent works toward deep-learning based facial forgery detection.• We provide a review of audio forgery detection as well as audio-visual forgery detection among the main - categories of forgery detection methods, which differs from most of previous survey.• We summarize…
Nikhil Sontakke, Sejal Utekar, Shivansh Rastogi, Shriraj Sonawane
Due to the widespread use of smartphones with high-quality digital cameras and easy access to a wide range of software apps for recording, editing, and sharing videos and images, as well as the deep learning AI platforms, a new phenomenon of 'faking' videos has emerged. Deepfake algorithms can create fake images and…
Kim Uittenhove, Hatef Otroshi Shahreza, Sébastien Marcel, Meike Ramon
Recent developments in generative AI offer the means to create synthetic identities, or deepfakes, at scale. As deepfake faces and voices become indistinguishable from real ones, they are considered as promising alternatives for research and development to enhance fairness and protect humans’ rights to privacy.…
Jiacheng Sun, Bingrui Li, Raghu Kalluri
Integrity of scientific data is critical in biomedical research, where images often serve as primary evidence for experimental observations and conclusions. Advances in image-editing technologies and generative artificial intelligence (AI) have increased the accessibility and realism of visual manipulation, making…
Thirza Dado, Yağmur Güçlütürk, Luca Ambrogioni, Gabriëlle Ras + 3 more
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring functional magnetic resonance imaging data as subjects…
Victor H. R. Nogueira, Rishabh Sharma, Rafael V. C. Guido, Michael J. Keiser
As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings…