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arxiv: 2509.15406 · v2 · submitted 2025-09-18 · 💻 cs.CV

Causal Fingerprints of AI Generative Models

Hui Xu , Chi Liu , Congcong Zhu , Minghao Wang , Youyang Qu , Longxiang Gao This is my paper

Pith reviewed 2026-05-18 15:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords causal fingerprintgenerative modelsmodel attributiondiffusion modelsGANforgery detectionsource anonymization
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The pith

AI generative models leave causal fingerprints that can be isolated from image content and style.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper seeks to establish that generative models imprint causal traces on images which reflect the underlying provenance rather than surface artifacts alone. If true, this would allow more reliable attribution of images to their source models even when content and style vary widely, supporting forgery detection and copyright tracing. The approach uses a causality-decoupling framework to extract these fingerprints in a semantic-invariant latent space obtained from pre-trained diffusion reconstruction residuals. Validation occurs through stronger attribution results on both GANs and diffusion models plus successful source anonymization via counterfactual examples generated from the fingerprints.

Core claim

A complete model fingerprint should reflect the causality between image provenance and model traces. This is achieved by a causality-decoupling framework that disentangles the fingerprint from image-specific content and style inside a semantic-invariant latent space derived from pre-trained diffusion reconstruction residual, with added granularity from diverse feature representations. The framework is validated by superior attribution performance across representative GANs and diffusion models and by effective source anonymization using counterfactual examples.

What carries the argument

Causality-decoupling framework that extracts the causal fingerprint from image content and style inside a semantic-invariant latent space derived from pre-trained diffusion reconstruction residual.

If this is right

  • Attribution performance improves across GANs and diffusion models compared with prior fingerprint methods.
  • Counterfactual images generated from the extracted causal fingerprints successfully anonymize the original source model.
  • The technique supports practical uses in forgery detection, model copyright tracing, and identity protection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same latent-space separation could be tested on video or audio generators to check whether causal traces generalize beyond still images.
  • If the fingerprints prove stable, platforms might adopt them as a standard layer for verifying or labeling AI-generated media.
  • Combining the approach with existing artifact-based detectors could produce hybrid systems that remain effective even when individual cues are removed.

Load-bearing premise

The pre-trained diffusion reconstruction residual produces a semantic-invariant latent space that isolates causal model traces independently of variations in image content and style.

What would settle it

Attribution accuracy would fall sharply when the method is applied to images whose content and style differ substantially from training examples or to entirely new generative models not seen during development.

read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper conceptualizes causal fingerprints of generative models as reflecting the causality between image provenance and model traces. It proposes a causality-decoupling framework that extracts these fingerprints by disentangling them from image-specific content and style within a semantic-invariant latent space derived from pre-trained diffusion reconstruction residuals, augmented by diverse feature representations. The approach is validated through attribution experiments across GANs and diffusion models and via source anonymization using counterfactual examples generated from the causal fingerprints, with claims of outperforming prior methods.

Significance. If the semantic-invariance of the diffusion residual latent space holds and the framework successfully isolates model-causal traces, the work could provide a more generalizable, causality-grounded alternative to artifact-based attribution techniques, with direct applications to forgery detection, model copyright tracing, and identity protection. The use of pre-trained diffusion models for residual extraction and the inclusion of source anonymization experiments represent potentially valuable contributions if supported by rigorous validation.

major comments (2)
  1. [Abstract] Abstract: The claim of validation 'across representative GANs and diffusion models' and 'outperforms existing methods in model attribution' is central to the paper's contribution, yet the abstract provides no quantitative metrics, error analysis, baseline comparisons, or dataset details, leaving the empirical support for the central claims unverifiable from the available text.
  2. [Framework description] Framework description (causality-decoupling section): The assertion that the pre-trained diffusion reconstruction residual produces a semantic-invariant latent space that isolates causal model traces independently of content and style variations is load-bearing for the entire disentanglement and attribution pipeline. No explicit invariance proof, ablation holding the generative model fixed while varying semantic content, or leakage analysis is described; if residuals retain content-specific reconstruction errors (e.g., object boundaries or texture statistics), the subsequent steps will confound content differences with model identity.
minor comments (1)
  1. [Introduction] Introduction: The positioning relative to prior model fingerprinting literature could be strengthened by explicitly contrasting the proposed causal approach against recent diffusion-specific attribution techniques.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. We address each major comment point by point below and outline the revisions we will implement to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim of validation 'across representative GANs and diffusion models' and 'outperforms existing methods in model attribution' is central to the paper's contribution, yet the abstract provides no quantitative metrics, error analysis, baseline comparisons, or dataset details, leaving the empirical support for the central claims unverifiable from the available text.

    Authors: We agree that the abstract would benefit from including concrete quantitative highlights to make the central empirical claims more immediately verifiable. In the revised manuscript, we will update the abstract to report key attribution accuracy figures across the evaluated GAN and diffusion models, note the main baselines compared against, and briefly reference the dataset scale and diversity used in the experiments. These additions will be kept concise while directly supporting the validation claims. revision: yes

  2. Referee: [Framework description] Framework description (causality-decoupling section): The assertion that the pre-trained diffusion reconstruction residual produces a semantic-invariant latent space that isolates causal model traces independently of content and style variations is load-bearing for the entire disentanglement and attribution pipeline. No explicit invariance proof, ablation holding the generative model fixed while varying semantic content, or leakage analysis is described; if residuals retain content-specific reconstruction errors (e.g., object boundaries or texture statistics), the subsequent steps will confound content differences with model identity.

    Authors: The referee rightly notes that our current validation of semantic invariance is indirect, relying on downstream attribution performance and counterfactual anonymization rather than a dedicated proof or controlled ablation. We will add a new ablation subsection in the revised manuscript that holds the generative model fixed while systematically varying semantic content and style (e.g., different object categories and scenes generated by the same model). We will also include a leakage analysis with both quantitative metrics on residual content retention and qualitative visualizations to show that content-specific features such as boundaries and textures are largely suppressed. These additions will provide more direct evidence for the isolation of model-causal traces. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework uses external pre-trained diffusion residuals

full rationale

The paper's central derivation begins with an external pre-trained diffusion model to extract reconstruction residuals, which are then used to form a semantic-invariant latent space for disentangling causal fingerprints from content and style. No equations or steps in the provided abstract or description reduce the claimed causal fingerprint or attribution results to fitted parameters, self-definitions, or self-citation chains by construction. Validation occurs via independent empirical checks on attribution performance across GANs and diffusion models, which does not presuppose the target result. This is a standard non-circular empirical proposal relying on external components.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that pre-trained diffusion reconstruction residuals create a suitable semantic-invariant space for causality disentanglement, with no free parameters or invented entities explicitly introduced in the abstract.

axioms (1)
  • domain assumption Pre-trained diffusion reconstruction residual yields a semantic-invariant latent space for decoupling causality from content and style
    Invoked to derive the space in which model fingerprints are isolated.

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · 4 internal anchors

  1. [1]

    Causal Fingerprints of AI Generative Models

    INTRODUCTION The rapid evolution of generative models has significantly improved AI-generated content (AIGC), particularly in producing highly real- istic images. However, this creates challenges for model attribution, which aims to identify the correct source model that generated an image. Model attribution is crucial for AIGC safety [1]: it offers an au...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Definition of Causal Fingerprint (CF) Model fingerprints are features within images generated by AIGC models, related to model architecture and algorithmic configuration

    METHOD 2.1. Definition of Causal Fingerprint (CF) Model fingerprints are features within images generated by AIGC models, related to model architecture and algorithmic configuration. They reflect causal representations within the generation process, stemming from its non-random nature. A generated imageXcom- prises contentC, styleS, and artefactsA. The fi...

    work page

  3. [3]

    pre-trained on ImageNet and extracted class-token features us- ing a pre-trained ViT model [21]; for the embedding space of the self-supervised learning method (SSL), we utilised the encoder head of pre-trained DINO ResNet50 [22]. By weighting and fusing the projection differences across these embedding spaces, the generated causal fingerprintF G comprehe...

    work page

  4. [4]

    EXPERIMENTS 3.1. Experimental Setup Datasets.To evaluate the performance of model attribution us- ing fingerprints across diverse environments, we constructed the ProGAN SD BigGANOriginal Artifact in RGBArtifact in DFT GlideOriginal Artifact in RGB Artifact in DFT Fig. 3. Examples of artefacts in extracted RGB and DFT spaces. Whereas QFT, SL, SSL and VSL,...

    work page 2019

  5. [5]

    By fo- cusing on underlying causal relationships, we propose a formalized causal decoupling method and define causal fingerprints, filling a gap in model forensics research

    CONCLUSION From a causal inference perspective, we investigate solutions to the attribution challenge in image source generation models. By fo- cusing on underlying causal relationships, we propose a formalized causal decoupling method and define causal fingerprints, filling a gap in model forensics research. Experiments validate the significant ad- vanta...

    work page

  6. [6]

    Chi Liu,Deep Image Forgery: An Investigation on Forensic and Anti- forensic Techniques, University of Technology Sydney (Australia), 2023

    work page 2023

  7. [7]

    Fighting malicious media data: A survey on tampering detection and deepfake detection,

    Junke Wang, Zhenxin Li, Chao Zhang, Jingjing Chen, Zuxuan Wu, Larry S Davis, and Yu-Gang Jiang, “Fighting malicious media data: A survey on tampering detection and deepfake detection,”Proceedings of the IEEE, 2025

    work page 2025

  8. [8]

    Fighting deepfakes by detecting gan dct anomalies,

    Oliver Giudice, Luca Guarnera, and Sebastiano Battiato, “Fighting deepfakes by detecting gan dct anomalies,”Journal of Imaging, vol. 7, no. 8, pp. 128, 2021

    work page 2021

  9. [9]

    Copyright in generative deep learning,

    Giorgio Franceschelli and Mirco Musolesi, “Copyright in generative deep learning,”Data & Policy, vol. 4, pp. e17, 2022

    work page 2022

  10. [10]

    Copyright safety for generative ai,

    Matthew Sag, “Copyright safety for generative ai,”Hous. L. Rev., vol. 61, pp. 295, 2023

    work page 2023

  11. [11]

    Black-box forgery attacks on semantic watermarks for diffusion models,

    Andreas M ¨uller, Denis Lukovnikov, Jonas Thietke, Asja Fischer, and Erwin Quiring, “Black-box forgery attacks on semantic watermarks for diffusion models,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 20937–20946

    work page 2025

  12. [12]

    Forensictransfer: Weakly- supervised domain adaptation for forgery detection,

    Davide Cozzolino, Justus Thies, Andreas R ¨ossler, Christian Riess, Matthias Nießner, and Luisa Verdoliva, “Forensictransfer: Weakly- supervised domain adaptation for forgery detection,”arXiv preprint arXiv:1812.02510, 2018

    work page arXiv 2018

  13. [13]

    Deep- fakeucl: Deepfake detection via unsupervised contrastive learning,

    Sheldon Fung, Xuequan Lu, Chao Zhang, and Chang-Tsun Li, “Deep- fakeucl: Deepfake detection via unsupervised contrastive learning,” in 2021 international joint conference on neural networks (IJCNN). IEEE, 2021, pp. 1–8

    work page 2021

  14. [14]

    Dfdt: an end-to-end deepfake detection framework using vision transformer,

    Aminollah Khormali and Jiann-Shiun Yuan, “Dfdt: an end-to-end deepfake detection framework using vision transformer,”Applied Sci- ences, vol. 12, no. 6, pp. 2953, 2022

    work page 2022

  15. [15]

    Identity- referenced deepfake detection with contrastive learning,

    Dongyao Shen, Youjian Zhao, and Chengbin Quan, “Identity- referenced deepfake detection with contrastive learning,” inProceed- ings of the 2022 ACM Workshop on Information Hiding and Multimedia Security, 2022, pp. 27–32

    work page 2022

  16. [16]

    Two- stream neural networks for tampered face detection,

    Peng Zhou, Xintong Han, Vlad I Morariu, and Larry S Davis, “Two- stream neural networks for tampered face detection,” in2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, 2017, pp. 1831–1839

    work page 2017

  17. [17]

    Dire for diffusion-generated image detection,

    Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li, “Dire for diffusion-generated image detection,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 22445–22455

    work page 2023

  18. [18]

    Cnn-generated images are surprisingly easy to spot... for now,

    Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros, “Cnn-generated images are surprisingly easy to spot... for now,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8695–8704

    work page 2020

  19. [19]

    Attributing fake images to gans: Learning and analyzing gan fingerprints,

    Ning Yu, Larry S Davis, and Mario Fritz, “Attributing fake images to gans: Learning and analyzing gan fingerprints,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 7556–7566

    work page 2019

  20. [20]

    Do gans leave artificial fingerprints?,

    Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi, “Do gans leave artificial fingerprints?,” in2019 IEEE con- ference on multimedia information processing and retrieval (MIPR). IEEE, 2019, pp. 506–511

    work page 2019

  21. [21]

    Detecting GAN-generated Imagery using Color Cues

    Scott McCloskey and Michael Albright, “Detecting gan-generated im- agery using color cues,”arXiv preprint arXiv:1812.08247, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  22. [22]

    Deepfake detection using deep learning methods: A systematic and comprehensive review,

    Arash Heidari, Nima Jafari Navimipour, Hasan Dag, and Mehmet Unal, “Deepfake detection using deep learning methods: A systematic and comprehensive review,”Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 14, no. 2, pp. e1520, 2024

    work page 2024

  23. [23]

    Man- ifpt: Defining and analyzing fingerprints of generative models,

    Hae Jin Song, Mahyar Khayatkhoei, and Wael AbdAlmageed, “Man- ifpt: Defining and analyzing fingerprints of generative models,” inPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 10791–10801

    work page 2024

  24. [24]

    Causaladv: Adversarial robustness through the lens of causality,

    Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xin- mei Tian, Bo Han, Bernhard Sch ¨olkopf, and Kun Zhang, “Causaladv: Adversarial robustness through the lens of causality,”arXiv preprint arXiv:2106.06196, 2021

    work page arXiv 2021

  25. [25]

    Deep residual learning for image recognition,

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep residual learning for image recognition,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770– 778

    work page 2016

  26. [26]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weis- senborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  27. [27]

    Emerging properties in self-supervised vision transformers,

    Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J ´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin, “Emerging properties in self-supervised vision transformers,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650–9660

    work page 2021

  28. [28]

    Learning transferable visual models from natural language supervision,

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., “Learning transferable visual models from natural language supervision,” inInternational conference on machine learning. PmLR, 2021, pp. 8748–8763

    work page 2021

  29. [29]

    Cross-attention is all you need: Adapting pretrained transformers for machine translation,

    Mozhdeh Gheini, Xiang Ren, and Jonathan May, “Cross-attention is all you need: Adapting pretrained transformers for machine translation,” arXiv preprint arXiv:2104.08771, 2021

    work page arXiv 2021

  30. [30]

    Towards Deep Learning Models Resistant to Adversarial Attacks

    Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, “Towards deep learning models resistant to adversarial attacks,”arXiv preprint arXiv:1706.06083, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  31. [31]

    Genim- age: A million-scale benchmark for detecting ai-generated image,

    Mingjian Zhu, Hanting Chen, Qiangyu Yan, Xudong Huang, Guanyu Lin, Wei Li, Zhijun Tu, Hailin Hu, Jie Hu, and Yunhe Wang, “Genim- age: A million-scale benchmark for detecting ai-generated image,”Ad- vances in Neural Information Processing Systems, vol. 36, pp. 77771– 77782, 2023

    work page 2023

  32. [32]

    Fourier spectrum discrepancies in deep network generated images,

    Tarik Dzanic, Karan Shah, and Freddie Witherden, “Fourier spectrum discrepancies in deep network generated images,”Advances in neural information processing systems, vol. 33, pp. 3022–3032, 2020

    work page 2020

  33. [33]

    Riemannian-geometric fingerprints of generative models,

    Hae Jin Song and Laurent Itti, “Riemannian-geometric fingerprints of generative models,”arXiv preprint arXiv:2506.22802, 2025

    work page arXiv 2025

  34. [34]

    The fr ´echet distance between multivariate normal distributions,

    DC Dowson and BV666017 Landau, “The fr ´echet distance between multivariate normal distributions,”Journal of multivariate analysis, vol. 12, no. 3, pp. 450–455, 1982

    work page 1982