From GAN-Based Models to GPT-5: A Taxonomy-Guided Study of Human Performance in Detecting Deepfake Face Images
Systematic study shows human ability to spot deepfakes degrades sharply with newer generative models, underscoring the need for automated detection in enterprise media-verification workflows.
Summary written by editorial AI · Source link below
arXiv:2610.23144v1 Announce Type: cross Abstract: Generative models for deepfakes have rapidly evolved from GAN-based architectures to diffusion models and multimodal large language models (MLLMs). However, empirical research on human performance in detecting deepfake face images has not kept pace. It remains unclear if (and how) detection performance varies across generative models and whether model-specific training improves detection more effectively than general-purpose training. In this pa
Editorial Analysis
As deepfake quality outpaces human perception, enterprises relying on manual media verification for fraud prevention or executive-impersonation defence face growing blind spots.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
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