arXiv:2608. 20336v1 Announce Type: new Abstract: Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people.
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces.
The paper introduces a benchmark and evaluation system for measuring how well generative image models preserve the identity of a subject across generation, editing, restoration, and multi‑subject scenarios. It compares three paradigms—input context, trainable subject‑specific parameters, and a persistent identity layer—showing that persistent identity consistently improves fidelity while keeping image quality and instruction adherence high. The study finds that identity preservation remains a distinct limitation of current foundation models, especially under iterative edits, small scales, severe degradation, and multi‑subject composition.
arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while...
arXiv:2601.01352v2 Announce Type: replace Abstract: Human identity-preserving text-to-video generation remains challenging under large changes in viewpoint, facial expression, illumination, and motio...
arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.
The paper introduces Anti-Persona, an image-level defense that prevents unauthorized identity binding and recognition in personalized vision–language models. By aggregating visual features from multiple reference images into an identity prototype and applying subtle perturbations, the method disrupts prototype alignment in the vision‑encoder space while maintaining visual fidelity. Experiments on two personalized LVLMs show protection rates up to 95.0% and robustness across prompt variations and encoder mismatches.
The paper examines the reliability of deepfake detectors, noting a decline in accuracy from 99.5% to 76% over four years and a drastic drop below 2% when adversarial perturbations are applied. It argues that content alone cannot determine provenance because generators can perfectly reproduce authentic images, so a new detection approach is proposed that certifies authenticity only if a generator cannot faithfully reconstruct the content. The authors demonstrate that their calibrated detector can limit false certifications to 1% while maintaining robustness against bounded‑perturbation attacks, though it struggles with arbitrary transformations.
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...
The paper evaluates twenty deepfake detectors against ten recent generators, finding that accuracy has dropped from 99.5% to 76% and that adversarial perturbations can reduce detector performance to below 2%. It proposes a new detection paradigm that certifies authenticity only when a faithful reconstruction by a known generator is impossible, and shows that this calibrated detector can limit false certifications to at most 1% while remaining robust to bounded‑perturbation attacks. The study also highlights the erosion of post‑hoc verifiability, noting that newer generators can reproduce a larger fraction of previously unreplicable images.
IDPrior introduces a two-stage framework that separates identity generation from facial image synthesis. First, it learns a diffusion prior in the embedding space of a pretrained face recognition model to model and generate novel identity embeddings. These embeddings then condition a pretrained identity-conditioned diffusion model to synthesize multiple images of the same identity, producing diverse and well-separated identities that align closely with authentic identity distributions.
arXiv:2505. 03201v4 Announce Type: replace-cross Abstract: Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential.