ALADDIN is a one‑step face‑swapping method that distills a pre‑trained multi‑step diffusion model into a fast generator trained adversarially with identity and target‑preservation constraints. It offers a controllable trade‑off between identity transfer and attribute preservation via triplet pseudo‑label supervision, and it outperforms other one‑step methods in realism and target preservation while matching the best multi‑step model with far fewer evaluations and training pairs. The authors also uncover a bias in standard evaluation protocols and re‑evaluate baselines with an independent identity encoder.
arXiv:2511.05575v2 Announce Type: replace Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based meth...
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
arXiv:2610.19717v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avat...
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
arXiv:2611.07941v1 Announce Type: new Abstract: Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly...
arXiv:2605.22311v2 Announce Type: replace Abstract: Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as...
Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly and raises privacy and ethical concerns. Existin...
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.
The paper introduces LI-Boost, a technique that enhances the local invariance of adversarial perturbations to improve their transferability across models. By demonstrating a positive correlation between local invariance and transferability, the authors show that LI-Boost significantly boosts five categories of transfer-based attacks on ImageNet, including CNNs, ViTs, defense mechanisms, commercial APIs, and vision-language models. Extensive experiments confirm the method’s effectiveness across diverse architectures and attack types.
The paper introduces Implicit Bridge Consistency Distillation (IBCD), a method that enables single-step, bidirectional unpaired image translation without adversarial loss. IBCD uses a diffusion implicit bridge model that connects PF-ODE trajectories between distributions, parametrized on a signed time axis to select the target domain, allowing one network to translate in both directions in a single step. The authors also propose distribution matching for consistency distillation and an adaptive weighting scheme based on distillation difficulty, achieving state‑of‑the‑art performance on benchmark datasets and extending to brain MRI contrast translation.
arXiv:2608.31053v1 Announce Type: new Abstract: Diffusion models have achieved strong results in high-fidelity image synthesis, but their iterative sampling process makes large-scale generation compu...
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.