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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.
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.