Results 81 to 90 of about 6,295 (185)
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
Modern AI systems can now synthesize coherent multimedia experiences, generating video and audio directly from text prompts. These unified frameworks represent a rapid shift toward controllable and synchronized content creation. From early neural architectures to transformer and diffusion paradigms, this paper contextualizes the ongoing evolution of ...
Charles Ding, Rohan Bhowmik
wiley +1 more source
Deepfake Detection Method Integrating Multiple Parameter-Efficient Fine-Tuning Techniques [PDF]
In 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
SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains.
Kyeong-Hwan Moon +2 more
doaj +1 more source
Audio Deepfake Detection: A Survey
Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved.
Tao, Jianhua +5 more
core
Deepfake Detection Using Deep Learning
Artificial intelligence is being used to create hyper-realistic synthetic media as well as misinformation, identity theft, and fraud. As deepfake techniques become more sophisticated, deepfake detection becomes more crucial. This paper explores the application of deep learning approaches for the detection of deepfakes.
null Dr. B. M Vidyavathi +3 more
openaire +1 more source
Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. Though technology has primarily been utilized for beneficial purposes,
Naveed Ur Rehman Ahmed +5 more
doaj +1 more source
Comprehensive multiparametric analysis of human deepfake speech recognition
In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research.
Kamil Malinka +5 more
doaj +1 more source
Does Current Deepfake Audio Detection Model Effectively Detect ALM-Based Deepfake Audio?
Currently, Audio Language Models (ALMs) are rapidly advancing due to the developments in large language models and audio neural codecs. These ALMs have significantly lowered the barrier to creating deepfake audio, generating highly realistic and diverse types of deepfake audio, which pose severe threats to society.
Xie, Yuankun +11 more
openaire +2 more sources
A Comparative Analysis of Compression and Transfer Learning Techniques in DeepFake Detection Models
DeepFake detection models play a crucial role in ambient intelligence and smart environments, where systems rely on authentic information for accurate decisions.
Andreas Karathanasis +2 more
doaj +1 more source

