Results 21 to 30 of about 12,433 (211)

Detection Enhancement for Various Deepfake Types Based on Residual Noise and Manipulation Traces

IEEE Access, 2022
As deepfake techniques become more sophisticated, the demand for fake facial image detection continues to increase. Various deepfake detection techniques have been introduced but detecting all types of deepfake images with a single model remains ...
Jihyeon Kang, Sang-Keun Ji, Sangyeong Lee, Daehee Jang, Jong-Uk Hou   +4 more
doaj   +1 more source

Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection [PDF]

Computer Vision and Pattern Recognition, 2022
Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen ...
Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, Jue Wang   +4 more
semanticscholar   +1 more source

Does Audio Deepfake Detection Generalize? [PDF]

Interspeech, 2022
Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techniques for detecting audio spoofs, it is often unclear exactly why these ...
N. Müller, Pavel Czempin, Franziska Dieckmann, Adam Froghyar, Konstantin Böttinger   +4 more
semanticscholar   +1 more source

Deepfake Generation and Detection: Case Study and Challenges

IEEE Access, 2023
In smart communities, social media allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and deep learning (DL) models have evolved.
Yogesh Patel, Sudeep Tanwar, Rajesh Gupta, Pronaya Bhattacharya, Innocent Ewean Davidson, Royi Nyameko, Srinivas Aluvala, Vrince Vimal   +7 more
doaj   +1 more source

DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAs

, 2023
Because of the availability of larger datasets and recent improvements in the generative model, more realistic Deepfake videos are being produced each day. People consume around one billion hours of video on social media platforms every day, and thats why it is very important to stop the spread of fake videos as they can be damaging, dangerous, and ...
Omkar Bhilare, Rahul Singh, Vedant Paranjape, Sravan Chittupalli, Shraddha Suratkar, Faruk Kazi 0001   +5 more
openaire   +2 more sources

Deepfake Technology: A Criminological Literature Review

Sakarya Üniversitesi Hukuk Fakültesi Dergisi/Sakarya Hukuk Dergisi, 2023
Cybercrimes are rapidly evolving with the advancement of technology and reaching new dimensions. Deepfake technology, which is the subject of this article, has also become a part of cybercrime.
Fatih Arslan
doaj   +1 more source

DeepFake-o-meter: An Open Platform for DeepFake Detection [PDF]

CoRR, 2021
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work,
Yuezun Li, Cong Zhang, Pu Sun 0001, Honggang Qi, Siwei Lyu   +4 more
openaire   +3 more sources

ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection

Mathematics, 2023
Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques.
Eun-Gi Lee, Isack Lee, Seok-Bong Yoo
doaj   +1 more source

ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild [PDF]

IEEE/ACM Transactions on Audio Speech and Language Processing, 2022
Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to ...
Xuechen Liu, Xin Wang, Md Sahidullah, J. Patino, Héctor Delgado, T. Kinnunen, M. Todisco, J. Yamagishi, N. Evans, A. Nautsch, Kong Aik LEE   +10 more
semanticscholar   +1 more source

Deepfake attack prevention using steganography GANs [PDF]

PeerJ Computer Science, 2022
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 ...
Iram Noreen, Muhammad Shahid Muneer, Saira Gillani   +2 more
doaj   +2 more sources