Results 171 to 180 of about 6,379 (204)
The continued influence of AI-generated deepfake videos despite transparency warnings. [PDF]
Commun PsycholClark S, Lewandowsky S.
europepmc +1 more source
A gated temporal attention based intra prediction framework for robust deepfake video detection. [PDF]
Sci RepClapten JE, Balaji V.
europepmc +1 more source
Explainable AI for forensic speech authentication within cognitive and computational neuroscience. [PDF]
Front NeurosciCheng Z, Yang H, Xiong Y, Hu X.
europepmc +1 more source
Detection of cloned voices in realistic forensic voice comparison scenarios. [PDF]
Front Artif IntellUnivaso P, San Segundo E.
europepmc +1 more source
M6: multi-generator, multi-domain, multi-lingual and cultural, multi-genres, multi-instrument machine-generated music detection databases. [PDF]
Sci RepLi Y, Li H, Specia L, Schuller B.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics, 2023
This tutorial presents developments on the detection of Deepfakes, which are realistic images, audios and videos created using deep learning techniques. Deepfakes can be readily used for malicious purposes and pose a serious threat to privacy and security.
Md Shohel Rana, Andrew H. Sung
openaire +1 more source
This tutorial presents developments on the detection of Deepfakes, which are realistic images, audios and videos created using deep learning techniques. Deepfakes can be readily used for malicious purposes and pose a serious threat to privacy and security.
Md Shohel Rana, Andrew H. Sung
openaire +1 more source
2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 2021
In recent years the Deep generative networks have made it easy to create real face swaps in images and videos with less traces of manipulation, significantly improving the quality of these deepfakes. This improvement in fake media have gained more concern as for their use in fake terrorism, blackmail etc.
Harsh Agarwal, Ankur Singh, Rajeswari D
openaire +1 more source
In recent years the Deep generative networks have made it easy to create real face swaps in images and videos with less traces of manipulation, significantly improving the quality of these deepfakes. This improvement in fake media have gained more concern as for their use in fake terrorism, blackmail etc.
Harsh Agarwal, Ankur Singh, Rajeswari D
openaire +1 more source
DEEPFAKE BUSTER: AI-POWERED DEEPFAKE DETECTION
Deepfake technology has rapidly advanced, making it difficult to distinguish between real and manipulated media.This poses serious risks, including misinformation, identity theft, and fraud. Our project, Deepfake Buster, aims todetect AI-generated deepfake videos using machine learning techniques.M. Shravan Kumar +3 more
openaire +1 more source
Deepfakes are a type of synthetic media that can be used to create realistic videos of people saying or doing things they never did. This raises concerns about the potential for deepfakes to be used to spread misinformation or propaganda. In this project, we present a deepfake detection module that can be used to identify deepfakes with high accuracy ...
Athawale, Prof. S. V. +4 more
openaire +1 more source
Athawale, Prof. S. V. +4 more
openaire +1 more source
In tech-enabled communities, social media allows users to access multimedia content easily. With recent advancements in computer vision and natural language processing, machine learning (ML) and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create synthetic media of a ...
Nisha Rose +4 more
openaire +1 more source
Nisha Rose +4 more
openaire +1 more source