Results 71 to 80 of about 6,210 (184)
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
, 2018 It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN).
Korshunov, Pavel, Marcel, Sebastien
core
SECURITY AND PRIVACY, Volume 9, Issue 1, January/February 2026.ABSTRACT Unarguably, malware and their variants have metamorphosed into objects of attack and cyber warfare. These issues have directed research focus to modeling infrastructural settings and infection scenarios, analyzing propagation mechanisms, and conducting studies that highlight optimized remedial measures.
Chukwunonso Henry Nwokoye
wiley +1 more source
Robust Sequential DeepFake Detection
, 2023 Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed.
Liu, Ziwei, Shao, Rui, Wu, Tianxing
core
Robustness Analysis of Distributed CNN Model Training in Expression Recognition
IET Biometrics, Volume 2026, Issue 1, 2026.Facial expression recognition is vital in pattern recognition and affective computing. With the advancement of deep learning, its performance has improved, yet challenges remain in nonlaboratory environments due to occlusion, poor lighting, and varying head poses.
Jun Li, Jun Wan
wiley +1 more source
Anomaly Detection of Deepfake Audio Based on Real Audio Using Generative Adversarial Network Model
IEEE AccessDeepfake audio causes damage not only to individuals and companies, but also to nations; therefore, research on deepfake audio detection technology is crucial.
Daeun Song +3 more
doaj +1 more source
Deepfake Detection Method Integrating Multiple Parameter-Efficient Fine-Tuning Techniques [PDF]
Jisuanji kexue yu tansuoIn recent years, as deepfake technology matures, face-swapping software and synthesized videos have become widespread. While these techniques offer entertainment, they also provide opportunities for misuse by malicious actors.
ZHANG Yiwen, CAI Manchun, CHEN Yonghao, ZHU Yi, YAO Lifeng
doaj +1 more source
MLC: Enhanced Deepfake Detection Through Multi‐Level Collaborations
IET Image Processing, Volume 20, Issue 1, January/December 2026.A novel Multi‐Level Collaborations (MLC) strategy to enhance the generalisation performance through simultaneously extracting and integrating three distinct levels of discriminative features during the encoding process. Three complementary forge clues: pixel‐level fine‐grained, region‐level facial layout, and semantic‐level deep clues, are integrated ...
Lihua Wang +4 more
wiley +1 more source
International Journal for Research in Applied Science and Engineering TechnologyAbstract: Deepfake technology has become a significant threat to the integrity of multimedia content, posing challenges to areas such as cybersecurity, media forensics, and information authenticity. To address this, our research introduces a Multimodal Deepfake Detection system capable of identifying manipulated content by combining visual and auditory
Prof. Sneha G +5 more
openaire +1 more source
The Deepfake Challenges and Deepfake Video Detection
International Journal of Innovative Technology and Exploring Engineering, 2020 Deepfake is a combination of fake and deep-learning technology. Deep learning is the function of artificial intelligence that can be used to create and detect deepfakes. Deepfakes are created using generative adversarial networks, in which two machine learning models exit.
openaire +1 more source
Artificial intelligence for deepfake detection: systematic review and impact analysis [PDF]
Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its
Sri Nagesh, Ayyagari +1 more
core +2 more sources