Advancing AI, Virtue Enhancement, and the Need for Niebuhrian Virtues of Moral Vigilance
Journal of Religious Ethics, Volume 54, Issue 3, Page 294-319, September 2026.ABSTRACT This paper considers the possibility of AI being employed to improve moral character from the perspective of an account of virtues that is psychologically realistic (the HEIS model). It is informed by a Christian agapeic ethic oriented toward goods of creaturely well‐being that ground right action.
Gregory R. Peterson
wiley +1 more source
Is this real? Susceptibility to deepfakes in machines and humans
Cognitive ResearchDeepfakes are synthetic media created by deep-generative methods to fake a person’s audio-visual representation. Growing sophistication of deepfake technology poses significant challenges for both machine learning (ML) algorithms and humans. Here we used
Didem Pehlivanoglu +7 more
doaj +1 more source
SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
Applied SciencesRecently, 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
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
VoiceRadar: Voice Deepfake Detection using Micro-Frequency and Compositional Analysis
Recent advancements in synthetic speech generation, including text-to-speech (TTS) and voice conversion (VC) models, allow the generation of convincing synthetic voices, often referred to as audio deepfakes.
Sadeghi, Ahmad-Reza +6 more
core +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 for Facial Images with Facemasks
, 2022 Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detection ...
Woo, Simon S. +5 more
core +1 more source
Comprehensive multiparametric analysis of human deepfake speech recognition
EURASIP Journal on Image and Video ProcessingIn 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
Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
IEEE AccessIn 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
Conditioned Prompt-Optimization for Continual Deepfake Detection [PDF]
The rapid advancement of generative models has significantly enhanced the realism and customization of digital content creation. The increasing power of these tools, coupled with their ease of access, fuels the creation of photorealistic fake content ...
Thomas De Min +3 more
core +3 more sources