How to Trace Latent Generative Model Generated Images without Artificial Watermark?

• URL: http://arxiv.org/abs/2405.13360v1
• Date: Wed, 22 May 2024 05:33:47 GMT
• Title: How to Trace Latent Generative Model Generated Images without Artificial Watermark?
• Authors: Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma,
• Abstract summary: 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.
• Score: 88.04880564539836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models. It is, therefore, necessary to analyze the origin of images by inferring if a particular image was generated by a specific latent generative model. Most existing methods (e.g., image watermark and model fingerprinting) require extra steps during training or generation. These requirements restrict their usage on the generated images without such extra operations, and the extra required operations might compromise the quality of the generated images. In this work, we ask whether it is possible to effectively and efficiently trace the images generated by a specific latent generative model without the aforementioned requirements. To study this problem, we design a latent inversion based method called LatentTracer to trace the generated images of the inspected model by checking if the examined images can be well-reconstructed with an inverted latent input. We leverage gradient based latent inversion and identify a encoder-based initialization critical to the success of our approach. Our experiments on the state-of-the-art latent generative models, such as Stable Diffusion, show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency. Our findings suggest the intriguing possibility that today's latent generative generated images are naturally watermarked by the decoder used in the source models. Code: https://github.com/ZhentingWang/LatentTracer.

Related papers

• Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models [71.13610023354967]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. We propose a diffusion model watermarking technique that is both performance-lossless and training-free.
  arXiv  Detail & Related papers  (2024-04-07T13:30:10Z)
• Active Generation for Image Classification [50.18107721267218]
  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)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• 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)
• Securing Deep Generative Models with Universal Adversarial Signature [69.51685424016055]
  Deep generative models pose threats to society due to their potential misuse. In this paper, we propose to inject a universal adversarial signature into an arbitrary pre-trained generative model. The proposed method is validated on the FFHQ and ImageNet datasets with various state-of-the-art generative models.
  arXiv  Detail & Related papers  (2023-05-25T17:59:01Z)
• Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis [8.777277201807351]
  We develop a new detection method for images that are indistinguishable from real ones. Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models. Our approach achieves comparable performance to state-of-the-art pre-trained detection methods on images generated by Stable Diffusion and Midversa.
  arXiv  Detail & Related papers  (2023-03-19T20:31:38Z)
• Ensembling with Deep Generative Views [72.70801582346344]
  generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars.
  arXiv  Detail & Related papers  (2021-04-29T17:58:35Z)

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.