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
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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.
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.
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.
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
Guidance watermarking steers diffusion denoising steps via gradients from an off-the-shelf watermark decoder to embed marks during generation, converting post-hoc schemes into in-generation ones while remaining complementary to VAE modifications.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
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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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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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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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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Guidance Watermarking for Diffusion Models
Guidance watermarking steers diffusion denoising steps via gradients from an off-the-shelf watermark decoder to embed marks during generation, converting post-hoc schemes into in-generation ones while remaining complementary to VAE modifications.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
- The Forensic Cost of Watermark Removal: From Dedicated Attacks to Image Editing