The paper introduces a new task, Intra-Architecture Source Attribution for Stable Diffusion (IntrAA‑SD), which focuses on distinguishing between fine‑tuned versions of the same Stable Diffusion XL architecture. It presents a 140K‑image benchmark dataset with in‑domain and out‑of‑distribution prompts, and proposes a baseline method using a style‑conditioned Mixture‑of‑Experts network that leverages a frozen CLIP backbone and a LoRA‑adapted DINOv2 network. The authors evaluate their approach on attribution accuracy and conduct ablation studies, providing code and data publicly on GitHub.
Designing systems that attribute a generated image to its source model has lately become a major area of AI research. Most existing datasets and systems related to image source attribution are targete...
The paper introduces a multi‑view, confusion‑guided ensemble framework for synthetic image attribution, combining FFT‑ConvNeXt, DINOv2, CLIP, and Xception to capture frequency, semantic, and forensic cues. Extensive data augmentation simulates realistic post‑processing, while a binary expert classifier and class‑adaptive confidence calibration address ambiguities between similar diffusion models. The approach achieved 99.53% on the public leaderboard and 99.20% on the private leaderboard for the ICANN 2026 DLMMDD Workshop challenge.
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.
arXiv:2608. 04030v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored.
arXiv:2510.03245v3 Announce Type: replace-cross Abstract: State-of-the-art attribution methods such as AttEXplore rely on adversarial sample generation that applies an all-pass filter across the freq...
CrossGMN introduces a graph metanetwork that processes a trained source network and an initialized target network simultaneously, enabling equivariant cross‑architecture weight‑space transformations. By preserving symmetry through cross‑network message passing, CrossGMN can refine target network initializations while remaining invariant to source permutations and equivariant to target permutations. Experiments demonstrate that CrossGMN accelerates knowledge distillation, transfers across datasets without retraining, and unifies compression from diverse source architectures into a common target architecture.
Moonworks Lunara is a text‑to‑image model that defines Artistic Intelligence as exploration‑driven world realization, preserving semantic, artistic, and compositional structure. It uses a Diffusion Mixture Transformer architecture and a training algorithm that iteratively refines the data distribution with informative samples and human‑created art. Benchmarks show Lunara ranks first in aesthetic quality and second in emotional resonance against seven other image‑generation models, while maintaining a sub‑10B parameter size and sub‑10‑second inference latency.
LongBanana is the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis, featuring 90 tasks each with about 32 reference images, 20 constraints, and 50 evaluation items, totaling 4,670 atomic evaluation items. The benchmark uses a graph-grounded evaluation design that converts entity-relation graph obligations into checklist items for Content Fidelity, Relational Binding, and Global Constraint Satisfaction. An accompanying agent-harness framework, LongBanana-Gen, demonstrates that minimal harnesses improve generation quality, with a ReAct-Plan-Exec-Reflect workflow further enhancing evidence grounding and repair.
arXiv:2511. 04260v3 Announce Type: replace-cross Abstract: The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems.
The paper revisits pixel-space diffusion models, showing that a vanilla diffusion transformer can generate directly in pixel space by swapping velocity prediction for clean image prediction. It introduces MFlow, an adaptive parameterization that interpolates between these two prediction modes based on data representation and model capacity, and demonstrates competitive performance on ImageNet, UCF‑101, and text‑to‑image tasks. Additionally, the authors add ConvRefiner, a lightweight convolutional module trained post‑hoc to restore high‑frequency details while preserving FID.