Training-free Source Attribution of AI-generated Images via Resynthesis

• URL: http://arxiv.org/abs/2510.24278v1
• Date: Tue, 28 Oct 2025 10:39:04 GMT
• Title: Training-free Source Attribution of AI-generated Images via Resynthesis
• Authors: Pietro Bongini, Valentina Molinari, Andrea Costanzo, Benedetta Tondi, Mauro Barni,
• Abstract summary: We present a new training-free one-shot attribution method based on image resynthesis.<n>We also introduce a new dataset for synthetic image attribution consisting of face images from commercial and open-source text-to-image generators.
• Score: 15.553070492553298
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthetic image source attribution is a challenging task, especially in data scarcity conditions requiring few-shot or zero-shot classification capabilities. We present a new training-free one-shot attribution method based on image resynthesis. A prompt describing the image under analysis is generated, then it is used to resynthesize the image with all the candidate sources. The image is attributed to the model which produced the resynthesis closest to the original image in a proper feature space. We also introduce a new dataset for synthetic image attribution consisting of face images from commercial and open-source text-to-image generators. The dataset provides a challenging attribution framework, useful for developing new attribution models and testing their capabilities on different generative architectures. The dataset structure allows to test approaches based on resynthesis and to compare them to few-shot methods. Results from state-of-the-art few-shot approaches and other baselines show that the proposed resynthesis method outperforms existing techniques when only a few samples are available for training or fine-tuning. The experiments also demonstrate that the new dataset is a challenging one and represents a valuable benchmark for developing and evaluating future few-shot and zero-shot methods.

Related papers

• OmniDFA: A Unified Framework for Open Set Synthesis Image Detection and Few-Shot Attribution [21.62979058692505]
  OmniDFA is a novel framework for AIGI that assesses the authenticity of images, and determines the origins in a few-shot manner.<n>We construct OmniFake, a large class-aware synthetic image dataset that curates $1.17$ M images from $45$ distinct generative models.<n>Experiments demonstrate that OmniDFA exhibits excellent capability in open-set attribution and achieves state-of-the-art generalization performance on AIGI detection.
  arXiv  Detail & Related papers  (2025-09-30T02:36:40Z)
• Dataset Distillation with Probabilistic Latent Features [9.318549327568695]
  A compact set of synthetic data can effectively replace the original dataset in downstream classification tasks.<n>We propose a novel approach that models the joint distribution of latent features.<n>Our method achieves state-of-the-art cross architecture performance across a range of backbone architectures.
  arXiv  Detail & Related papers  (2025-05-10T13:53:49Z)
• Attributed Synthetic Data Generation for Zero-shot Domain-specific Image Classification [3.0105723746073]
  We propose AttrSyn, which leverages large language models to generate attributed prompts.<n>Experiments for zero-shot domain-specific image classification on two fine-grained datasets show that training with synthetic images generated by AttrSyn significantly outperforms CLIP's zero-shot classification.
  arXiv  Detail & Related papers  (2025-04-06T14:54:10Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Diversified in-domain synthesis with efficient fine-tuning for few-shot classification [64.86872227580866]
  Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
  arXiv  Detail & Related papers  (2023-12-05T17:18:09Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• Explore the Power of Synthetic Data on Few-shot Object Detection [27.26215175101865]
  Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. Recent text-to-image generation models have shown promising results in generating high-quality images. This work extensively studies how synthetic images generated from state-of-the-art text-to-image generators benefit FSOD tasks.
  arXiv  Detail & Related papers  (2023-03-23T12:34:52Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• Re-Imagen: Retrieval-Augmented Text-to-Image Generator [58.60472701831404]
  Retrieval-Augmented Text-to-Image Generator (Re-Imagen) Retrieval-Augmented Text-to-Image Generator (Re-Imagen)
  arXiv  Detail & Related papers  (2022-09-29T00:57:28Z)
• Diverse Semantic Image Synthesis via Probability Distribution Modeling [103.88931623488088]
  We propose a novel diverse semantic image synthesis framework. Our method can achieve superior diversity and comparable quality compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-11T18:59:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.