Persistent Watermarking of Text-to-Image Models

arXiv:2609.39024 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/openai/consistencydecoder

Terminology

• Verification — 26× in this paper · explained in 2 episodes
• Diffusion Model — 21× in this paper · explained in 8 episodes
• Distillation — 20× in this paper · explained in 2 episodes
• Diffusion Models — 19× in this paper · explained in 6 episodes
• Pruning — 16× in this paper · explained in 4 episodes
• Adaptation — 15× in this paper · explained in 1 episode
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Quantization — 11× in this paper · explained in 12 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Personalization — 6× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Embeddings — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Provenance — 4× in this paper · explained in 4 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Consistency Distillation — 3× in this paper · explained in 2 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Machine Unlearning — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• aMUSEd: An Open MUSE Reproduction
• A Recipe for Watermarking Diffusion Models
• Detecting Language Model Attacks with Perplexity
• Removing Watermarks with Partial Regeneration using Semantic Information
• Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image
• Stable Signature is Unstable: Removing Image Watermark from Diffusion Models
• Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models

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