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arxiv: 2606.28386 · v1 · pith:R3QT7SVXnew · submitted 2026-06-22 · 💻 cs.CV · cs.AI

Data Provenance for Image Auto-Regressive Generation

Bihe Zhao , Louis Kerner , Michel Meintz , Tameem Bakr , Franziska Boenisch , Adam Dziedzic This is my paper

Pith reviewed 2026-06-30 10:26 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords data provenanceimage autoregressive modelspost-hoc detectionprovenance tracingimage generationforensic attributionnext-token prediction
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The pith

Image autoregressive models leave characteristic patterns in outputs that enable reliable post-hoc provenance tracing without modifications.

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

The paper shows that image autoregressive models, which generate images via next-token prediction, embed detectable patterns during synthesis even when the results look photorealistic. These patterns act as a built-in signal for tracing an image back to its source model. The authors introduce a post-hoc framework that identifies the patterns after generation is complete. This approach works on already-published images and on models that lack built-in watermarks. It addresses needs for attribution in cases involving misinformation or harmful content.

Core claim

Although IAR-generated images often appear visually identical to real images, their generation process introduces characteristic patterns in their outputs, which serves as a reliable provenance signal for the generated images. Leveraging this, the paper presents a post-hoc framework that enables the robust detection of such patterns for provenance tracing without requiring modifications of the generative process or outputs.

What carries the argument

The post-hoc detection framework that extracts and matches characteristic patterns introduced by the autoregressive next-token prediction process across IAR models.

If this is right

  • Provenance tracing becomes possible for IAR outputs that were published without any added marks.
  • The method applies to models that do not incorporate watermarking during generation.
  • Detection works across a wide range of IAR architectures without retraining the generators.
  • Attribution supports efforts to trace misinformation or harmful content to specific models.

Where Pith is reading between the lines

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

  • The same pattern-based approach could extend to other autoregressive generation tasks such as video or audio sequences.
  • Combining the detector with existing image forensics tools might improve robustness against evasion attempts.
  • Future work could test whether adversarial fine-tuning of IAR models can erase the detectable patterns.

Load-bearing premise

The characteristic patterns are sufficiently consistent, model-specific, and robust to post-processing that a detector can reliably attribute images to their source model across diverse IAR architectures and real-world conditions.

What would settle it

A test in which images from multiple IAR models undergo standard post-processing such as JPEG compression or cropping and the detector's attribution accuracy falls to chance levels.

Figures

Figures reproduced from arXiv: 2606.28386 by Adam Dziedzic, Bihe Zhao, Franziska Boenisch, Louis Kerner, Michel Meintz, Tameem Bakr.

Figure 1
Figure 1. Figure 1: Data Provenance: Token Space. Since the generated tokens of a given IAR are sampled from the codebook entries, the codebook acts as a key to distinguish the token representations of generated images from those of real images. We further enhance the reliability of our framework by amplifying existing signals and integrating ad￾ditional, carefully designed ones. First, we train a model to approximate the inv… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our data provenance framework for IARs. (1) During Generation, the tokens are generated by the autoregressive model, dequantized to a feature map, and decoded to a generated image. (2) Our Decoder Inversion aims at creating an inverse decoder that recovers the generated feature map from the generated image. (3) We propose two signals for our Data Prove￾nance: QuantLoss between the feature map r… view at source ↗
read the original abstract

Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models. As these models become widely accessible, robust data provenance is required to reliably trace IAR-generated images to the source model that synthesized them. This is critical to prevent the spread of misinformation, detect fraud, and attribute harmful content. We find that although IAR-generated images often appear visually identical to real images, their generation process introduces characteristic patterns in their outputs, which serves as a reliable provenance signal for the generated images. Leveraging this, we present a post-hoc framework that enables the robust detection of such patterns for provenance tracing. Notably, our framework does not require modifications of the generative process or outputs. Thereby, it is applicable in contexts where prior watermarking methods cannot be used, such as for generated content that is already published without additional marks and for models that do not integrate watermarking. We demonstrate the effectiveness of our approach across a wide range of IARs, highlighting its high potential for robust data provenance tracing in autoregressive image generation.

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

1 major / 0 minor

Summary. The manuscript claims that image autoregressive models (IARs) introduce characteristic patterns in their outputs that serve as reliable post-hoc provenance signals, even when images appear visually identical to real ones. It presents a framework for detecting these patterns to trace generated images to their source model without any modifications to the generative process or outputs, making it applicable to already-published content and models without built-in watermarking. The approach is described as effective and robust across a wide range of IARs.

Significance. If the empirical patterns prove consistent, model-specific, and robust to post-processing, the post-hoc detection framework could fill an important gap in data provenance for autoregressive image generation where watermarking is infeasible. The emphasis on applicability to existing content is a practical strength. However, the provided abstract supplies no quantitative results, error rates, dataset details, or baselines, preventing assessment of whether the central claim holds.

major comments (1)
  1. [Abstract] Abstract: the claim of effectiveness and robustness across a wide range of IARs is asserted without any quantitative results, validation details, error rates, or comparison baselines. Full paper evidence is required to evaluate whether the patterns actually support the claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their comments. We address the single major comment point-by-point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of effectiveness and robustness across a wide range of IARs is asserted without any quantitative results, validation details, error rates, or comparison baselines. Full paper evidence is required to evaluate whether the patterns actually support the claim.

    Authors: The abstract is intentionally concise and follows standard practice by summarizing contributions at a high level without numerical details. The full manuscript contains the required quantitative evidence: the Experiments section reports detection accuracies, false positive/negative rates, robustness evaluations under post-processing (e.g., compression, resizing), dataset specifications (including number of images and models tested), and comparisons against baseline provenance methods. These results directly support the claims of effectiveness and robustness across multiple IARs. We are willing to incorporate a brief summary of key metrics into the abstract if the editor requests it. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript describes an empirical detection framework for characteristic patterns in IAR outputs that serve as provenance signals. No equations, parameter-fitting steps, self-citations, or derivation chains appear in the abstract or summary material. The central claim rests on observed post-hoc patterns rather than any quantity defined in terms of itself or reduced to fitted inputs by construction. The approach is presented as model-agnostic and applicable to already-published content, confirming the finding is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the claim rests on an empirical observation of generation patterns.

pith-pipeline@v0.9.1-grok · 5745 in / 1050 out tokens · 30059 ms · 2026-06-30T10:26:15.446340+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

228 extracted references · 52 canonical work pages · 19 internal anchors

  1. [1]

    Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13 , pages=

    Microsoft coco: Common objects in context , author=. Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13 , pages=. 2014 , organization=

    2014

  2. [2]

    International Conference on Machine Learning , pages=

    A watermark for large language models , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  3. [3]

    European Conference on Computer Vision , pages=

    Rotary position embedding for vision transformer , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  4. [4]

    The Thirteenth International Conference on Learning Representations , year=

    Image and Video Tokenization with Binary Spherical Quantization , author=. The Thirteenth International Conference on Learning Representations , year=

  5. [5]

    Advances in neural information processing systems , volume=

    Visual autoregressive modeling: Scalable image generation via next-scale prediction , author=. Advances in neural information processing systems , volume=

  6. [6]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

  7. [7]

    Gaussian Mixture Models

    Reynolds, Douglas. Gaussian Mixture Models. Encyclopedia of Biometrics. 2009. doi:10.1007/978-0-387-73003-5_196

    work page doi:10.1007/978-0-387-73003-5_196 2009

  8. [8]

    Algorithmic Learning Theory , publisher =

    Learning with Deep Cascades , isbn =. Algorithmic Learning Theory , publisher =. doi:10.1007/978-3-319-24486-0_17 , series =

    work page doi:10.1007/978-3-319-24486-0_17

  9. [9]

    Koltchinskii and D

    V. Koltchinskii and D. Panchenko , title =. The Annals of Statistics , number =. 2002 , doi =

    2002

  10. [10]

    Performance Measures for Neyman–Pearson Classification , volume =

    Scott, Clayton , date =. Performance Measures for Neyman–Pearson Classification , volume =. doi:10.1109/TIT.2007.901152 , abstract =

    work page doi:10.1109/tit.2007.901152 2007

  11. [11]

    Concrete Problems in AI Safety

    Concrete problems in AI safety , author=. arXiv preprint arXiv:1606.06565 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

    Benchmarking neural network robustness to common corruptions and surface variations , author=. arXiv preprint arXiv:1807.01697 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Adversarial Examples Are a Natural Consequence of Test Error in Noise

    Adversarial examples are a natural consequence of test error in noise , author=. arXiv preprint arXiv:1901.10513 , year=

    work page internal anchor Pith review Pith/arXiv arXiv 1901

  14. [14]

    Advances in Neural Information Processing Systems , volume=

    Failing loudly: An empirical study of methods for detecting dataset shift , author=. Advances in Neural Information Processing Systems , volume=

  15. [15]

    Learning with Rejection , volume =

    Cortes, Corinna and. Learning with Rejection , volume =. Algorithmic Learning Theory , publisher =. doi:10.1007/978-3-319-46379-7_5 , note =

    work page doi:10.1007/978-3-319-46379-7_5

  16. [16]

    and Nowak, R

    Scott, C. and Nowak, R. , date =. A Neyman-Pearson approach to statistical learning , volume =. doi:10.1109/TIT.2005.856955 , abstract =

    work page doi:10.1109/tit.2005.856955 2005

  17. [17]

    Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples , url =

    Goldwasser, Shafi and Kalai, Adam Tauman and Kalai, Yael and Montasser, Omar , booktitle =. Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples , url =

  18. [18]

    2019 , eprint=

    Combining p-values via averaging , author=. 2019 , eprint=

    2019

  19. [19]

    International Conference on Learning Representations , year=

    Towards Deep Learning Models Resistant to Adversarial Attacks , author=. International Conference on Learning Representations , year=

  20. [20]

    Fleet , title =

    Sara Sabour and Yanshuai Cao and Fartash Faghri and David J. Fleet , title =. 4th International Conference on Learning Representations,. 2016 , url =

    2016

  21. [21]

    International Conference on Machine Learning , pages=

    On calibration of modern neural networks , author=. International Conference on Machine Learning , pages=. 2017 , organization=

    2017

  22. [22]

    international conference on machine learning , pages=

    Dropout as a bayesian approximation: Representing model uncertainty in deep learning , author=. international conference on machine learning , pages=. 2016 , organization=

    2016

  23. [23]

    International Conference on Machine Learning , pages=

    Weight uncertainty in neural network , author=. International Conference on Machine Learning , pages=. 2015 , organization=

    2015

  24. [24]

    University of Cambridge , volume=

    Uncertainty in deep learning , author=. University of Cambridge , volume=

  25. [25]

    Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

    Simple and scalable predictive uncertainty estimation using deep ensembles , author=. arXiv preprint arXiv:1612.01474 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  26. [26]

    2019 IEEE Security and Privacy Workshops (SPW) , pages=

    On the robustness of deep k-nearest neighbors , author=. 2019 IEEE Security and Privacy Workshops (SPW) , pages=. 2019 , organization=

    2019

  27. [27]

    2020 IEEE Security and Privacy Workshops (SPW) , pages=

    Minimum-Norm Adversarial Examples on KNN and KNN based Models , author=. 2020 IEEE Security and Privacy Workshops (SPW) , pages=. 2020 , organization=

    2020

  28. [28]

    Summer school on machine learning , pages=

    Gaussian processes in machine learning , author=. Summer school on machine learning , pages=. 2003 , organization=

    2003

  29. [29]

    Evasion Attacks against Machine Learning at Test Time

    Biggio, Battista and Corona, Igino and Maiorca, Davide and Nelson, Blaine and S rndi \' c , Nedim and Laskov, Pavel and Giacinto, Giorgio and Roli, Fabio. Evasion Attacks against Machine Learning at Test Time. Machine Learning and Knowledge Discovery in Databases. 2013

    2013

  30. [30]

    Explaining and Harnessing Adversarial Examples

    Explaining and harnessing adversarial examples , author=. arXiv preprint arXiv:1412.6572 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  31. [31]

    Proceedings of the 36th International Conference on Machine Learning , pages =

    The Odds are Odd: A Statistical Test for Detecting Adversarial Examples , author =. Proceedings of the 36th International Conference on Machine Learning , pages =. 2019 , editor =

    2019

  32. [32]

    2014 , URL =

    Intriguing properties of neural networks , author =. 2014 , URL =

    2014

  33. [33]

    International Conference on Learning Representations , year=

    Understanding the failure modes of out-of-distribution generalization , author=. International Conference on Learning Representations , year=

  34. [34]

    McDaniel , title =

    Nicolas Papernot and Patrick D. McDaniel , title =. CoRR , volume =. 2018 , url =

    2018

  35. [35]

    2009 , isbn =

    Koller, Daphne and Friedman, Nir , title =. 2009 , isbn =

    2009

  36. [36]

    Proceedings of the 36th International Conference on Machine Learning , pages =

    Analyzing and Improving Representations with the Soft Nearest Neighbor Loss , author =. Proceedings of the 36th International Conference on Machine Learning , pages =. 2019 , editor =

    2019

  37. [37]

    A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

    A baseline for detecting misclassified and out-of-distribution examples in neural networks , author=. arXiv preprint arXiv:1610.02136 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  38. [38]

    arXiv preprint arXiv:2007.15147 , year=

    Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers , author=. arXiv preprint arXiv:2007.15147 , year=

    work page arXiv 2007

  39. [39]

    , author=

    To Trust Or Not To Trust A Classifier. , author=. NeurIPS , pages=

  40. [40]

    arXiv preprint arXiv:1910.00727 , year=

    Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering , author=. arXiv preprint arXiv:1910.00727 , year=

    work page arXiv 1910

  41. [41]

    arXiv preprint arXiv:2101.06549 , year=

    AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles , author=. arXiv preprint arXiv:2101.06549 , year=

    work page arXiv

  42. [42]

    arXiv preprint arXiv:2103.07403 , year=

    Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles , author=. arXiv preprint arXiv:2103.07403 , year=

    work page arXiv

  43. [43]

    International Conference on Machine Learning , pages=

    Delayed impact of fair machine learning , author=. International Conference on Machine Learning , pages=. 2018 , organization=

    2018

  44. [44]

    Fair lending needs explainable models for responsible recommendation

    Fair lending needs explainable models for responsible recommendation , author=. arXiv preprint arXiv:1809.04684 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  45. [45]

    Black Hat , year=

    Evading machine learning malware detection , author=. Black Hat , year=

  46. [46]

    Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

    Distributed variational inference in sparse Gaussian process regression and latent variable models , author=. arXiv preprint arXiv:1402.1389 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  47. [47]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages=

    Evaluating scalable bayesian deep learning methods for robust computer vision , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages=

  48. [48]

    arXiv preprint arXiv:2102.12967 , year=

    Statistical Testing for Efficient Out of Distribution Detection in Deep Neural Networks , author=. arXiv preprint arXiv:2102.12967 , year=

    work page arXiv

  49. [49]

    A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , url =

    Lee, Kimin and Lee, Kibok and Lee, Honglak and Shin, Jinwoo , booktitle =. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , url =

  50. [50]

    Cohere, https://cohere.ai

  51. [51]

    OpenAI, https://openai.com

  52. [52]

    Zhang and A

    Florian Tramèr and F. Zhang and A. Juels and M. Reiter and T. Ristenpart. , title=. USENIX Security Symposium , year=

  53. [53]

    Courville and P

    Yoshua Bengio and A. Courville and P. Vincent. , title=. ArXiv , year=

  54. [54]

    Uchida and S

    Yuki Nagai and Y. Uchida and S. Sakazawa and Shin’ichi Satoh. , title=. International Journal of Multimedia Information Retrieval, 7:3–16 , year=

  55. [55]

    Hengrui Jia and C. A. Choquette-Choo and V. Chandrasekaran and N. Papernot. , title=. USENIX Security Symposium , year=

  56. [56]

    Kornblith and M

    Ting Chen and S. Kornblith and M. Norouzi and G. Hinton. , title=. International Conference on Machine Learning , year=

  57. [57]

    Fan and Y

    Kaiming He and H. Fan and Y. Wu and S. Xie and R. Girshick. , title=. Computer Vision and Pattern Recognition , year=

  58. [58]

    Strub and F

    Jean-Bastien Grill and F. Strub and F. Altché and C. Tallec and P. H. Richemond and E. Buchatskaya and C. Doersch and B. A. Pires and Z. D. Guo and M. G. Azar and B. Piot and K. Kavukcuoglu and R. Munos and M. Valko. , title=. Computer Vision and Pattern Recognition , year=

  59. [59]

    Jialong Zhang and Zhongshu Gu and Jiyong Jang and Hui Wu and M. P. Stoecklin and H. Huang and I. Molloy. , title=

  60. [60]

    , author=

    Representation Learning with Contrastive Predictive Coding. , author=. ArXiv , year=

  61. [61]

    , author=

    High Accuracy and High Fidelity Extraction of Neural Networks. , author=. USENIX Security Symposium , year=

  62. [62]

    , author=

    Learning Transferable Visual Models From Natural Language Supervision. , author=. Arxiv , year=

  63. [63]

    , author=

    Learning Fair Representations. , author=. International Conference on Machine Learning , year=

  64. [64]

    , author=

    Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective. , author=. Special Interest Group on Knowledge Discovery and Data Mining. , year=

  65. [65]

    2022 , eprint=

    Text and Code Embeddings by Contrastive Pre-Training , author=. 2022 , eprint=

    2022

  66. [66]

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    Bert: Pre-training of deep bidirectional transformers for language understanding , author=. arXiv preprint arXiv:1810.04805 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  67. [67]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Knockoff nets: Stealing functionality of black-box models , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  68. [68]

    2020 , eprint=

    Exploring Simple Siamese Representation Learning , author=. 2020 , eprint=

    2020

  69. [69]

    Supervised Contrastive Learning , url =

    Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip , booktitle =. Supervised Contrastive Learning , url =

  70. [70]

    2021 , eprint=

    10 Security and Privacy Problems in Self-Supervised Learning , author=. 2021 , eprint=

    2021

  71. [71]

    Proceedings of ICLR 2021: 9th International Conference on Learning Representationsn , year=

    Dataset inference: Ownership resolution in machine learning , author=. Proceedings of ICLR 2021: 9th International Conference on Learning Representationsn , year=

    2021

  72. [72]

    2019 IEEE European Symposium on Security and Privacy (EuroS&P) , pages=

    PRADA: protecting against DNN model stealing attacks , author=. 2019 IEEE European Symposium on Security and Privacy (EuroS&P) , pages=. 2019 , organization=

    2019

  73. [73]

    International Conference on Learning Representations , year=

    Increasing the Cost of Model Extraction with Calibrated Proof of Work , author=. International Conference on Learning Representations , year=

  74. [74]

    arXiv preprint arXiv:2002.12200 , year=

    Entangled watermarks as a defense against model extraction , author=. arXiv preprint arXiv:2002.12200 , year=

    work page arXiv 2002

  75. [75]

    International Conference on Learning Representations , year=

    Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks , author=. International Conference on Learning Representations , year=

  76. [76]

    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , pages=

    Towards reverse-engineering black-box neural networks , author=. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , pages=. 2019 , publisher=

    2019

  77. [77]

    Decentralized Business Review , pages=

    Bitcoin: A peer-to-peer electronic cash system , author=. Decentralized Business Review , pages=

  78. [78]

    arXiv preprint arXiv:2103.05633 , year=

    Proof-of-Learning: Definitions and Practice , author=. arXiv preprint arXiv:2103.05633 , year=

    work page arXiv

  79. [79]

    arXiv preprint arXiv:1906.00830 , year=

    Dawn: Dynamic adversarial watermarking of neural networks , author=. arXiv preprint arXiv:1906.00830 , year=

    work page arXiv 1906

  80. [80]

    Yossi Adi and Carsten Baum and Moustapha Cisse and Benny Pinkas and Joseph Keshet , title =. 27th. 2018 , isbn =

    2018

Showing first 80 references.