arXiv Machine Learning
Jun 10
Toward Calibrated, Fair, and accurate Deepfake Detection
arXiv:2606. 09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups.
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.
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arXiv Machine Learning
Jul 1
WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
Hugging Face Trending Papers
Aug 10
CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis
Fairness evaluation in computer vision commonly relies on aggregate accuracy and demographic subgroup analysis. However, visual models are also sensitive to contextual factors such as illumination, blur, image quality, facial accessories, and appearance attributes.
arXiv Computer Vision
Aug 27
Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation
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.
arXiv Machine Learning
Jun 30
$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.
arXiv AI
Aug 19
FairNVT: Fair Classification via Noise Injection in Vision Transformers
FairNVT is a lightweight debiasing framework that injects calibrated Gaussian noise into sensitive embeddings learned by adapters for pretrained transformer-based encoders. By reducing sensitive-attribute leakage through orthogonality constraints and fairness regularization, it improves fairness metrics such as demographic parity difference and equalized odds while maintaining competitive task performance across vision and language datasets.
arXiv AI
Aug 25
Fairness-Aware Mixture-of-Experts via Subgroup Reweighting and Gate Regularization
The paper proposes a fairness-aware Mixture-of-Experts (MoE) framework that tackles routing-induced bias by applying subgroup reweighting to correct data imbalance and gate entropy regularization to prevent the gating network from collapsing onto subgroup attributes. This end-to-end approach keeps expert utilization balanced and interpretable, offering a clear view of how subgroups are allocated across experts. Experiments show that the method improves fairness while maintaining competitive predictive performance.
arXiv AI
Jul 9
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
arXiv:2607. 06603v1 Announce Type: cross Abstract: The notion of algorithmic fairness has been actively explored from various aspects of fairness, such as counterfactual fairness (CF) and group fairness (GF).
arXiv Machine Learning
Jul 17
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
arXiv:2607. 14705v1 Announce Type: new Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race.
arXiv Computer Vision
6d ago
Benchmarking Spatial, Spectral, and Self-Supervised Cues for Face Forgery Detection under Realistic Degradation
arXiv:2609.01511v1 Announce Type: new Abstract: Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited....
arXiv AI
Aug 3
DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
arXiv AI
Jun 4
Adaptive Calibration for Fair and Performant Facial Recognition
arXiv:2606. 04469v1 Announce Type: cross Abstract: We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities.