LoRA-Key creates a standalone user-specific Watermark LoRA trained with a latent watermark prior and GOP, attachable via training-free superposition to protect LoRA ownership while preserving quality.
arXiv preprint arXiv:1909.01285 , year=
7 Pith papers cite this work. Polarity classification is still indexing.
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GIFGuard is the first spatiotemporal watermarking framework for proactive deepfake forensics in facial GIFs, using a 3D adaptive residual encoder and hourglass decoder plus a new GIFfaces dataset.
Re-watermarking reliably removes existing watermarks across 96 dataset-victim-attack combinations and pairs with a classifier achieving 0.878-0.953 accuracy, cutting bit accuracy by 25-48%.
CAT trains watermark detectors against adaptive compositional adversaries using differentiable attack selection, yielding up to 63.5% capacity gains on hard attacks versus random-augmentation baselines.
PGID restores watermark detection in diffusion models by using progressive inversion-denoising cycles to correct latents displaced by removal or forgery attacks.
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
Randomized per-query key selection with single-key detection acceptance bounds forgery success rate independently of collected samples while preserving model utility.
citing papers explorer
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LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models
LoRA-Key creates a standalone user-specific Watermark LoRA trained with a latent watermark prior and GOP, attachable via training-free superposition to protect LoRA ownership while preserving quality.
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GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking
GIFGuard is the first spatiotemporal watermarking framework for proactive deepfake forensics in facial GIFs, using a 3D adaptive residual encoder and hourglass decoder plus a new GIFfaces dataset.
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Watermarks Attack Watermarks: Re-Watermarking as a Generic Removal Strategy
Re-watermarking reliably removes existing watermarks across 96 dataset-victim-attack combinations and pairs with a classifier achieving 0.878-0.953 accuracy, cutting bit accuracy by 25-48%.
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Compositional Adversarial Training for Robust Visual Watermarking
CAT trains watermark detectors against adaptive compositional adversaries using differentiable attack selection, yielding up to 63.5% capacity gains on hard attacks versus random-augmentation baselines.
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PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection
PGID restores watermark detection in diffusion models by using progressive inversion-denoising cycles to correct latents displaced by removal or forgery attacks.
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Towards Robust Content Watermarking Against Removal and Forgery Attacks
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
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Mitigating Watermark Forgery in Generative Models via Randomized Key Selection
Randomized per-query key selection with single-key detection acceptance bounds forgery success rate independently of collected samples while preserving model utility.