Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution

• URL: http://arxiv.org/abs/2404.02697v2
• Date: Thu, 18 Jul 2024 13:25:04 GMT
• Title: Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
• Authors: Fengyuan Liu, Haochen Luo, Yiming Li, Philip Torr, Jindong Gu,
• Abstract summary: In this work, we study the origin attribution of generated images in a practical setting. The goal is to check if a given image is generated by the source model. We propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification.
• Score: 23.974575820244944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image's source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL-E 3 API verifies the real-world applicability of our solution.

Related papers

• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks. Our approach enables versatile capabilities via different inference-time sampling schemes. Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [88.04880564539836]
  Concerns have arisen regarding potential misuse related to images generated by latent generative models. We propose a latent inversion based method called LatentTracer to trace the generated images of the inspected model. Our experiments show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-05-22T05:33:47Z)
• BOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution [22.81354665006496]
  Synthetic image attribution addresses the problem of tracing back the origin of images produced by generative models. We propose a framework for open set attribution of synthetic images, named BOSC, that relies on the concept of backdoor attacks.
  arXiv  Detail & Related papers  (2024-05-19T09:17:43Z)
• Active Generation for Image Classification [45.93535669217115]
  We propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation.
  arXiv  Detail & Related papers  (2024-03-11T08:45:31Z)
• Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models [39.33821502730661]
  We present a training-free method to attribute fake images generated by text-to-image models to their source models. By calculating and ranking the similarity of the test image and the candidate images, we can determine the source of the image.
  arXiv  Detail & Related papers  (2024-03-03T11:55:49Z)
• Alteration-free and Model-agnostic Origin Attribution of Generated Images [28.34437698362946]
  Concerns have emerged regarding potential misuse of image generation models. It is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model.
  arXiv  Detail & Related papers  (2023-05-29T01:35:37Z)
• One-Shot Adaptation of GAN in Just One CLIP [51.188396199083336]
  We present a novel single-shot GAN adaptation method through unified CLIP space manipulations. Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization. We show that our model generates diverse outputs with the target texture and outperforms the baseline models both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-03-17T13:03:06Z)
• Meta Internal Learning [88.68276505511922]
  Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
  arXiv  Detail & Related papers  (2021-10-06T16:27:38Z)

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.