Results 171 to 180 of about 12,433 (211)

C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection

AAAI Conference on Artificial Intelligence
This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such as CLIP, are effective for generalizable deepfake detection along with ...
Chuangchuang Tan, Renshuai Tao, Huan Liu, Guanghua Gu, Baoyuan Wu, Yao Zhao, Yunchao Wei   +6 more
semanticscholar   +1 more source

Efficient Deepfake Detection via Layer-Frozen Assisted Dual Attention Network for Consumer Imaging Devices

IEEE transactions on consumer electronics
The advancement of open-source frameworks and user-friendly manipulation applications has accelerated the spread of deep fakes. In this study, we proposed optimal features assisted with a dual attention (DA) network strategy to combat this proliferation ...
Muhammad Talha Usman, Habib Khan, Sushil Kumar Singh, Mi Young Lee, Jakeoung Koo   +4 more
semanticscholar   +1 more source

Deepfake false memories

Memory, 2021
Machine-learning has enabled the creation of "deepfake videos"; highly-realistic footage that features a person saying or doing something they never did. In recent years, this technology has become more widespread and various apps now allow an average social-media user to create a deepfake video which can be shared online.
Gillian Murphy, Emma Flynn
openaire   +2 more sources

DF40: Toward Next-Generation Deepfake Detection

Neural Information Processing Systems
We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors
Zhiyuan Yan, Taiping Yao, Shen Chen, Yandan Zhao, Xinghe Fu, Junwei Zhu, Donghao Luo, Li Yuan, Chengjie Wang, Shouhong Ding, Yunsheng Wu   +10 more
semanticscholar   +1 more source

EnvSDD: Benchmarking Environmental Sound Deepfake Detection

Interspeech
Audio generation systems now create very realistic soundscapes that can enhance media production, but also pose potential risks. Several studies have examined deepfakes in speech or singing voice.
Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Haohe Liu, Wenwu Wang, Mark D. Plumbley   +6 more
semanticscholar   +1 more source

Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics

arXiv.org
The rapid advancement of AI technologies has significantly increased the diversity of DeepFake videos circulating online, posing a pressing challenge for \textit{generalizable forensics}, \ie, detecting a wide range of unseen DeepFake types using a ...
Yuezun Li, Delong Zhu, Xinjie Cui, Siwei Lyu   +3 more
semanticscholar   +1 more source

DeepFake Disrupter: The Detector of DeepFake Is My Friend

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Xueyu Wang, Jiajun Huang, Siqi Ma, Surya Nepal, Chang Xu 0002   +4 more
openaire   +1 more source

DeepFake's Adversary: Disrupting DeepFake by Perturbations

2022
In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would cause severe social impact and become a real threat to not only celebrities but also ordinary people.
openaire   +1 more source

Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection

North American Chapter of the Association for Computational Linguistics
This paper conducts a comprehensive layer-wise analysis of self-supervised learning (SSL) models for audio deepfake detection across diverse contexts, including multilingual datasets (English, Chinese, Spanish), partial, song, and scene-based deepfake ...
Y. E. Kheir, Younes Samih, Suraj Maharjan, Tim Polzehl, Sebastian Möller   +4 more
semanticscholar   +1 more source

Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection

arXiv.org
This paper tackles the challenge of detecting partially manipulated facial deepfakes, which involve subtle alterations to specific facial features while retaining the overall context, posing a greater detection difficulty than fully synthetic faces.
Andrii Yermakov, Jan Cech, Jiri Matas
semanticscholar   +1 more source