Causal Fingerprints of AI Generative Models

• URL: http://arxiv.org/abs/2509.15406v1
• Date: Thu, 18 Sep 2025 20:33:27 GMT
• Title: Causal Fingerprints of AI Generative Models
• Authors: Hui Xu, Chi Liu, Congcong Zhu, Minghao Wang, Youyang Qu, Longxiang Gao,
• Abstract summary: We argue that a complete model fingerprint should reflect the causality between image provenance and model traces.<n>We propose a causality-decoupling framework that disentangles it from image-specific content and style.<n>Our approach outperforms existing methods in model attribution, indicating strong potential for forgery detection, model copyright tracing, and identity protection.
• Score: 18.85839181425287
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for source attribution. Prior methods rely on model-specific cues or synthesis artifacts, yielding limited fingerprints that may generalize poorly across different generative models. We argue that a complete model fingerprint should reflect the causality between image provenance and model traces, a direction largely unexplored. To this end, we conceptualize the \emph{causal fingerprint} of generative models, and propose a causality-decoupling framework that disentangles it from image-specific content and style in a semantic-invariant latent space derived from pre-trained diffusion reconstruction residual. We further enhance fingerprint granularity with diverse feature representations. We validate causality by assessing attribution performance across representative GANs and diffusion models and by achieving source anonymization using counterfactual examples generated from causal fingerprints. Experiments show our approach outperforms existing methods in model attribution, indicating strong potential for forgery detection, model copyright tracing, and identity protection.

Related papers

• A Behavioral Fingerprint for Large Language Models: Provenance Tracking via Refusal Vectors [43.11304710234668]
  We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment.<n>In a large-scale identification task across 76 offspring models, our method achieves 100% accuracy in identifying the correct base model family.<n>We propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact.
  arXiv  Detail & Related papers  (2026-02-10T05:57:35Z)
• Learning Counterfactually Decoupled Attention for Open-World Model Attribution [75.52873383916672]
  We propose a Counterfactually Decoupled Attention Learning (CDAL) method for open-world model attribution.<n>Our method consistently improves state-of-the-art models by large margins, particularly for unseen novel attacks.
  arXiv  Detail & Related papers  (2025-06-29T03:25:45Z)
• Revealing the Implicit Noise-based Imprint of Generative Models [71.94916898756684]
  This paper presents a novel framework that leverages noise-based model-specific imprint for the detection task.<n>By aggregating imprints from various generative models, imprints of future models can be extrapolated to expand training data.<n>Our approach achieves state-of-the-art performance across three public benchmarks including GenImage, Synthbuster and Chameleon.
  arXiv  Detail & Related papers  (2025-03-12T12:04:53Z)
• 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)
• ManiFPT: Defining and Analyzing Fingerprints of Generative Models [16.710998621718193]
  We formalize the definition of artifact and fingerprint in generative models. We propose an algorithm for computing them in practice. We study the structure of the fingerprints and observe that it is very predictive of the effect of different design choices on the generative process.
  arXiv  Detail & Related papers  (2024-02-16T01:58:35Z)
• Model Synthesis for Zero-Shot Model Attribution [26.835046772924258]
  generative models are shaping various fields such as art, design, and human-computer interaction.<n>We propose a model synthesis technique, which generates numerous synthetic models mimicking the fingerprint patterns of real-world generative models.<n>Our experiments demonstrate that this fingerprint extractor, trained solely on synthetic models, achieves impressive zero-shot generalization on a wide range of real-world generative models.
  arXiv  Detail & Related papers  (2023-07-29T13:00:42Z)
• Attributing Image Generative Models using Latent Fingerprints [33.037718660732544]
  Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. One potential risk mitigation strategy is to attribute generative models via fingerprinting. This paper investigates the use of latent semantic dimensions as fingerprints.
  arXiv  Detail & Related papers  (2023-04-17T00:13:10Z)
• Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints [14.191129493685212]
  Existing methods approximate the distance between the models via their sample distributions. We consider unique traces (a.k.a. "artificial fingerprints") as representations of generative models. We propose a new learning method based on set-encoding and contrastive training.
  arXiv  Detail & Related papers  (2022-06-04T23:20:07Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Responsible Disclosure of Generative Models Using Scalable Fingerprinting [70.81987741132451]
  Deep generative models have achieved a qualitatively new level of performance. There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale. Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
  arXiv  Detail & Related papers  (2020-12-16T03:51:54Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.