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BitMark: Watermarking Bitwise Autoregressive Image Generative Models

NeurIPS 2025 · Image generation › Autoregressive and latent generation · approach: Robust diffusion watermarking · 0 citations (OpenAlex)

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Proposes BitMark, a bit-level generation-time watermark for bitwise autoregressive image models (Infinity, Instella) that biases green bit-sequences, detected by z-test on re-encoded images, showing robustness and radioactivity.

Problem

Watermarking images from bitwise autoregressive text-to-image models so model owners can identify their own generated outputs and avoid model collapse, with watermarks that are robust to removal and radioactive (persist when other models are trained on them).

Approach

Adapt KGW-style green/red list soft biasing from language models to the bit level: partition n-bit sequences into green and red lists and add a logit bias to bits that complete green sequences during generation; detection counts green bit sequences in re-encoded images with a z-test. Bit-level watermarking is more stable under decode-re-encode than token-level.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
apply BitMark (δ=2) during generationInfinity-2B · image quality on MS-COCO 2014 validationFID: 33.36 -> 31.05Helped
increase logit bias from δ=3 to δ=5 (ablation)Infinity-2B BitMark · image quality on MS-COCO 2014 validationFID: 29.61 -> 127.44Hurt
apply BitMark (δ=2)Infinity-2B · CtrlRegen+TPR@1%FPR: 1.1 -> 91.6Helped
fine-tune Stable Diffusion 2.1 on Infinity-2B watermarked imagesStable Diffusion 2.1 · radioactivityTPR@1%FPR: 98.9Helped
fine-tune Infinity-2B with 10% watermarked dataInfinity-2B · radioactivityTPR@1%FPR: 22.7%Helped
use green list {11, 00} instead of {01, 10} (ablation)BitMark on Infinity-2B · watermark detectionAverage z-score: 90.33 -> 91.35No clear effect
reduce logit bias from δ=2 to δ=1 (ablation)BitMark on Infinity-2B · CtrlRegen+TPR@1%FPR: 91.6 -> 61.9Hurt
increase logit bias from δ=2 to δ=3 (ablation)BitMark on Infinity-2B · CtrlRegen+TPR@1%FPR: 91.6 -> 97.0Helped
add rotation correction before detection (ablation)BitMark on Infinity-2B · random rotations from 0° to 360°TPR@1%FPR: 18.4 -> 98.3Helped
apply watermark only to scales 1–10 instead of scales 1–13 (ablation)BitMark on Infinity-2B · CtrlRegen+TPR@1%FPR: 91.6 -> 82.1Hurt
apply watermark only to scales 11–13 instead of scales 1–13 (ablation)BitMark on Infinity-2B · CtrlRegen+TPR@1%FPR: 91.6 -> 15.5Hurt
increase logit bias from δ=2 to δ=5 (ablation)BitMark on Infinity-2B · CLIP Score on MS-COCO 2014 validationCLIP Score: 31.15 (0.28) -> 26.13 (0.40)Hurt

Surprising findings

Limitations

Experimental setup

Datasets: MS-COCO 2014 validation

Benchmarks: MS-COCO 2014 validation

Metrics: TPR@1%FPR FID KID CLIP score z-score AUC accuracy

Baselines: RivaGAN StegaStamp TrustMark

Builds on: KGW watermark Infinity Tree-Ring Unigram-Watermark

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