Results 161 to 170 of about 12,433 (211)
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.
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EFIMD-Net: Enhanced Feature Interaction and Multi-Domain Fusion Deep Forgery Detection Network. [PDF]
J ImagingCheng H +5 more
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Awareness to Deepfake: A resistance mechanism to Deepfake
2021 International Congress of Advanced Technology and Engineering (ICOTEN), 2021The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered.
Mohammad Faisal Bin Ahmed +3 more
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UCF: Uncovering Common Features for Generalizable Deepfake Detection
IEEE International Conference on Computer Vision, 2023Deepfake detection remains a challenging task due to the difficulty of generalizing to new types of forgeries. This problem primarily stems from the overfitting of existing detection methods to forgery-irrelevant features and method-specific patterns ...
Zhiyuan Yan +3 more
semanticscholar +1 more source
2021
Deepfakes—manipulated or synthetic audiovisual media, mostly created using AI—are employed in a wide range of contexts: from politics to pornography, crime, business, law enforcement, art, satire, education and activism. This study provides the first holistic technology assessment of the societal and ethical implications of deepfakes in these contexts ...
Maria Pawelec, Cora Bieß
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Deepfakes—manipulated or synthetic audiovisual media, mostly created using AI—are employed in a wide range of contexts: from politics to pornography, crime, business, law enforcement, art, satire, education and activism. This study provides the first holistic technology assessment of the societal and ethical implications of deepfakes in these contexts ...
Maria Pawelec, Cora Bieß
openaire +1 more source
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
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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
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Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
arXiv.orgIn the age of increasingly realistic generative AI, robust deepfake detection is essential for mitigating fraud and disinformation. While many deepfake detectors report high accuracy on academic datasets, we show that these academic benchmarks are out of
Nuria Alina Chandra +12 more
semanticscholar +1 more source
2020
Deepfakes. The technology, which enables the manipulation of video and audio files, in combination with fake news has caused a lot of insecurities in the last several years. Deepfakes use neural networks to insert people in hypothetical scenarios.
openaire +3 more sources
Deepfakes. The technology, which enables the manipulation of video and audio files, in combination with fake news has caused a lot of insecurities in the last several years. Deepfakes use neural networks to insert people in hypothetical scenarios.
openaire +3 more sources
Computer Vision and Pattern Recognition
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in ...
Zhenglin Huang +8 more
semanticscholar +1 more source
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in ...
Zhenglin Huang +8 more
semanticscholar +1 more source