← Back to all news
arXiv Computer Vision By Subir Kumar Parida, Rajbabu Velmurugan, Ketan Kotwal, R. S. Sengar, Swati Hiremath

Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation

Read the original on arXiv Computer Vision →

The paper introduces Semantic Boundary Predictor (SBP), an inference‑time framework that improves demographic fairness in synthetic face generation by applying a single, one‑shot intervention during reverse denoising. SBP learns linear semantic boundaries from late‑stage latent representations and applies them only at the initial noisy latent, leaving the rest of the diffusion process unchanged. Experiments on CelebA‑HQ show significant reductions in fairness disparity—98% for gender, 95% for binary race, and 15% for four‑class race—while preserving image quality across demographic groups.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 16

DenseFace: Bias Mitigation in Face Recognition via Density-Aware Probabilistic Matching

DenseFace introduces a bias‑mitigation technique for face recognition that operates on pre‑trained models without retraining. It models each person’s face embeddings with a von Mises‑Fisher distribution and uses a density‑aware probabilistic matching procedure to account for demographic differences. Experiments show that DenseFace consistently reduces racial bias across various architectures and datasets while preserving recognition accuracy.

By Mansur Bultygov, Vadim Seliutin, Dmitry Nekhaev, Ivan Laptev
ragsafety
More like this →
arXiv Computer Vision
Sep 1

FairReL: Deepfake Detection using Fairness-Aware Representation Learning

FairReL is a fairness‑aware representation‑learning framework for deepfake detection that targets two subgroup‑sensitive components: multi‑scale spatial features and fine‑tuning‑induced residual features. It uses an SVD‑decomposed backbone to isolate residuals and introduces Group‑Conditional Wavelet Decorrelation (GCWD) and Subspace‑Localised Mean Alignment (SLMA) losses to suppress subgroup imbalance and align subgroup means. Experiments on FF++, Celeb‑DF, DFD, and DFDC show that FairReL improves unseen‑dataset AUC by 3.9% and reduces subgroup FPR disparity by 10.2% compared to the state‑of‑the‑art fairness‑aware detector.

By Xiaoman Lu, Jiaqi Li, Shuntian Zheng, Huiping Chen, Yu Guan
fine-tuningbenchmarkssafety
More like this →