A theoretical framework decouples diffusion model generation from watermark decisions, enabling SSB to reach any security-robustness-fidelity regime without model-specific empirical tests.
Zeki Yalniz, and Alexandre Mourachko
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
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.
Watermark removal leaves statistical artifacts that allow classifiers to detect the attempt at 10^{-3} FPR across tested methods, establishing forensic stealthiness as a required property.
LAVA is a layered audio-visual watermarking system using cross-modal fusion and calibration-aware alignment to achieve robust deepfake tamper detection and localization under compression and asynchrony.
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
citing papers explorer
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Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles
A theoretical framework decouples diffusion model generation from watermark decisions, enabling SSB to reach any security-robustness-fidelity regime without model-specific empirical tests.
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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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The Forensic Cost of Watermark Removal
Watermark removal leaves statistical artifacts that allow classifiers to detect the attempt at 10^{-3} FPR across tested methods, establishing forensic stealthiness as a required property.
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LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization
LAVA is a layered audio-visual watermarking system using cross-modal fusion and calibration-aware alignment to achieve robust deepfake tamper detection and localization under compression and asynchrony.
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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.