mlsanityData to 14 Oct 2026

PALADIN : Robust Neural Fingerprinting for Text-to-Image Diffusion Models

arXiv 2506.03170 ↗First posted 28 May 2025cs.CV cs.AI cs.LGcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

The risk of misusing text-to-image generative models for malicious uses, especially due to the open-source development of such models, has become a serious concern. As a risk mitigation strategy, attributing generative models with neural fingerprinting is emerging as a popular technique. There has been a plethora of recent work that aim for addressing neural fingerprinting. A trade-off between the attribution accuracy and generation quality of such models has been studied extensively. None of the existing methods yet achieved 100% attribution accuracy. However, any model with less than cent percent accuracy is practically non-deployable. In this work, we propose an accurate method to incorporate neural fingerprinting for text-to-image diffusion models leveraging the concepts of cyclic error correcting codes from the literature of coding theory.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 13 of its 16 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

13 of 16 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Attack-Resilient Image Watermarking Using Stable Diffusion8 Jan
  2. 2023WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models7 Jun
  3. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Models27 Mar

Other references · 10

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  5. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  6. 2018Joint Autoregressive and Hierarchical Priors for Learned Image Compressioncited by 664
  7. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
  8. 2020Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Datacited by 201
  9. 2018Context-adaptive Entropy Model for End-to-end Optimized Image Compressioncited by 143
  10. 2020Responsible Disclosure of Generative Models Using Scalable Fingerprintingcited by 91

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models
  2. 2023Attributing Image Generative Models using Latent Fingerprints
  3. 2023Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis
  4. 2026Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
  5. 2024Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey
  6. 2023Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
  7. 2025Causal Fingerprints of AI Generative Models
  8. 2023Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks