Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.
Face Deepfakes -- A Comprehensive Review
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In recent years, remarkable advancements in deep-fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Face-Trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators
Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.