Results 161 to 170 of about 130,964 (204)

Face Forgery Detection Based on Deep Learning

2020
In this paper, we propose two deep learning approaches for face forgery detection. The first approach uses neural networks to detect fake faces in individual images. Three methods of this approach are studied. The second approach uses long short-term memory recurrent neural networks to detect fake videos by checking inconsistencies between successive ...
Yih-Kai Lin, Ching-Yu Chang
openaire   +1 more source

SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection

Neural Information Processing Systems
Detection of face forgery videos remains a formidable challenge in the field of digital forensics, especially the generalization to unseen datasets and common perturbations.
Yachao Liang, Min Yu, Gang Li, Jianguo Jiang, Boquan Li, Feng Yu, Ning Zhang, Xiang Meng, Weiqing Huang   +8 more
semanticscholar   +1 more source

MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection

IEEE Transactions on Dependable and Secure Computing
Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN-based face forgery detectors, ViT-based methods take advantage of the expressivity of transformers, achieving superior detection performance ...
Chenqi Kong, Anwei Luo, Peijun Bao, Yi Yu, Haoliang Li, Zengwei Zheng, Shiqi Wang, A. Kot   +7 more
semanticscholar   +1 more source

Distilling Multi-Level Semantic Cues Across Multi-Modalities for Face Forgery Detection

IEEE transactions on circuits and systems for video technology (Print)
Existing face forgery detection methods attempt to identify low-level forgery artifacts (e.g., blending boundary, flickering) in spatial-temporal domains or high-level semantic inconsistencies (e.g., abnormal lip movements) between visual-auditory ...
Lingyun Yu, Tian Xie, Chuanbin Liu, Guoqing Jin, Zhiguo Ding, Hongtao Xie   +5 more
semanticscholar   +1 more source

Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection

arXiv.org
Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training data.This work introduces Agent4FaceForgery to address two fundamental ...
Yingxin Lai, Zitong Yu, Jun Wan, Linlin Shen, Yong Xu, Xiaochun Cao   +5 more
semanticscholar   +1 more source

Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery Detection

IEEE Transactions on Information Forensics and Security
Although existing face forgery detection methods achieve satisfactory performance under closed within-dataset scenario where training and testing sets are created by the same manipulation technique, they are vulnerable to samples created by unseen ...
Jiawei Liu, Jingyi Xie, Yang Wang, Zheng-Jun Zha   +3 more
semanticscholar   +1 more source

Face Forgery Detection via Symmetric Transformer

Proceedings of the 30th ACM International Conference on Multimedia, 2022
Luchuan Song, Xiaodan Li, Zheng Fang, Zhenchao Jin, YueFeng Chen, Chenliang Xu   +5 more
openaire   +1 more source

Multi-attention Based Face Forgery Detection

2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC), 2023
Xiang Han, Yongfeng Qi, Liqiang Zhuang, Liang Hu, Shengcong Wen   +4 more
openaire   +1 more source

Generalizing Face Forgery Detection by Suppressed Texture Network With Two-Branch Convolution

IEEE Transactions on Computational Social Systems
With the development of Internet technology, deepfake (DF) videos can spread rapidly through online platforms, providing a new way of cyberbullying by generating nude pictures of female victims and using their faces to generate pornographic movies, which
Dengyong Zhang, Daijie Li, A. K. Sangaiah, Feng Li, Zelin Deng, Chengcheng Wu   +5 more
semanticscholar   +1 more source

CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection

International Joint Conference on Artificial Intelligence
With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection.
Binjia Zhou, Hengrui Lou, Lizhe Chen, Haoyuan Li, Dawei Luo, Shuai Chen, Jie Lei, Zunlei Feng, Yijun Bei   +8 more
semanticscholar   +1 more source