SEED is a new benchmark for sequential provenance tracing in diffusion-edited deepfake faces, with the FAITH baseline showing that wavelet-based high-frequency signals aid detection of accumulated editing artifacts.
Ledits: Real image editing with ddpm inversion and semantic guidance
3 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
MakeupMirror reports 60% better facial similarity and 50% less skin tone change than Stable-Makeup using geometry, region, and tone controls in diffusion models.
citing papers explorer
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SEED: A Large-Scale Benchmark for Provenance Tracing in Sequential Deepfake Facial Edits
SEED is a new benchmark for sequential provenance tracing in diffusion-edited deepfake faces, with the FAITH baseline showing that wavelet-based high-frequency signals aid detection of accumulated editing artifacts.
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UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
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MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer
MakeupMirror reports 60% better facial similarity and 50% less skin tone change than Stable-Makeup using geometry, region, and tone controls in diffusion models.