Results 71 to 80 of about 130,964 (204)

DeepFaceLab: A simple, flexible and extensible face swapping framework

, 2020
DeepFaceLab is an open-source deepfake system created by \textbf{iperov} for face swapping with more than 3,000 forks and 13,000 stars in Github: it provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of ...
Chervoniy, Nikolay, Dpfks, Mr., Facenheim, Carl Shift, Gao, Daiheng, Jiang, Jian, Liu, Kunlin, Marangonda, Sugasa, Perov, Ivan, RP, Luis, Umé, Chris, Wu, Pingyu, Zhang, Sheng, Zhang, Weiming, Zhou, Bo   +13 more
core  

Face Forgery Detection Using Deep Learning

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
The rising occurrence of forged content poses a threat to the authenticity of multimedia. This research suggests a simplified hybrid architecture as an effective method for detecting face forgeries. The framework includes CNN for dependable classification, EfficientNet for robust feature extraction, and MTCNN for precise face detection.
Shruthi T V, Rachana K R, Bhuvana S, Neha Appaji Y, Purnima R   +4 more
openaire   +1 more source

Difficulty‑Aware Meta‑Learning for Cross‑Domain Face Forgery Detection

Nanjing Hangkong Hangtian Daxue Xuebao
With the rapid iteration of facial forgery techniques， robust detection mechanisms that can handle unseen forgery methods are increasingly crucial. However， current approaches are primarily tailored to specific forgery techniques， posing limitations in ...
JIN Shichen, TAN Xiaoyang
doaj   +1 more source

Multi-attention-based approach for deepfake face and expression swap detection and localization

EURASIP Journal on Image and Video Processing, 2023
Advancements in facial manipulation technology have resulted in highly realistic and indistinguishable face and expression swap videos. However, this has also raised concerns regarding the security risks associated with deepfakes.
Saima Waseem, Syed Abdul Rahman Syed Abu-Bakar, Zaid Omar, Bilal Ashfaq Ahmed, Saba Baloch, Adel Hafeezallah   +5 more
doaj   +1 more source

Learning spatial‐frequency interaction for generalizable deepfake detection

IET Image Processing
In recent years, face forgery detection has gained significant attention, resulting in considerable advancements. However, most existing methods rely on CNNs to extract artefacts from the spatial domain, overlooking the pervasive frequency‐domain ...
Tianbo Zhai, Kaiyin Lu, Jiajun Li, Yukai Wang, Wenjie Zhang, Peipeng Yu, Zhihua Xia   +6 more
doaj   +1 more source

DeepFake Detection Method Based on Multi-Scale Dual-Stream Network [PDF]

Jisuanji gongcheng
DeepFake-enabled abuse of face forgery technology has given rise to considerable security risks to society and individuals; therefore, DeepFake detection has become a hot topic of research. Current deep learning-based forgery detection techniques exhibit
JIANG Cuiling, CHENG Ziyuan, YU Xingui, WAN Yongjing
doaj   +1 more source

ID3: Identity-Driven Learning Based on 3D Reconstruction and Frame-Level Residual Enhancement for Deepfakes Detection

IET Biometrics
With the rapid advancement of face manipulation technology, various forged videos of celebrities and politicians have appeared and cause pernicious social impact. In this light, forge video detection becomes a research hot spot recently.
Hui Ma, Jin Zhang, Xihong Chen, Wenhao Chu, Jiabao Guo, Junze Zheng, Liying Yang, Yanyan Liang   +7 more
doaj   +1 more source

Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection

Applied Sciences
The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio.
Zishuo Guo, Baopeng Zhang, Jack Fan, Zhu Teng, Jianping Fan   +4 more
doaj   +1 more source

Face Forgery Detection with Elaborate Backbone

Face Forgery Detection (FFD), or Deepfake detection, aims to determine whether a digital face is real or fake. Due to different face synthesis algorithms with diverse forgery patterns, FFD models often overfit specific patterns in training datasets, resulting in poor generalization to other unseen forgeries. This severe challenge requires FFD models to
Guo, Zonghui, Liu, Yingjie, Zhang, Jie, Zheng, Haiyong, Shan, Shiguang   +4 more
openaire   +2 more sources

Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization

, 2023
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localizing manipulated areas. Nonetheless, with limited fine-grained pixel-wise supervision labels, deepfake detection models perform unsatisfactorily ...
Lai, Yingxin, Luo, Zhiming, Yu, Zitong
openaire   +2 more sources