SafeMark integrates a thresholded watermark-decoding loss into diffusion editors to enable text-guided edits that preserve embedded watermarks with high bit accuracy.
Waves: Bench- marking the robustness of image watermarks
6 Pith papers cite this work. Polarity classification is still indexing.
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W-IR is the first watermarking framework to combine certified robustness via randomized smoothing in pixel and coordinate spaces with identity leakage mitigation via residual information loss minimization.
Dual-Guard embeds complementary watermarks in diffusion image generation to verify provenance and localize tampering with low error rates on a 2400-sample benchmark under reprompting and editing attacks.
Major watermarking benchmarks omit cross-lingual, cultural, and demographic reporting, creating a pluralistic evaluation gap that current governance mandates ignore.
C2PA manifests and AI watermarks can independently validate contradictory claims on the same asset, and a cross-layer audit protocol resolves this with 100% accuracy on 3500 images.
SEAL uses semantic embeddings and locality-sensitive hashing to create distortion-free, database-free watermarks for generative images that are conditioned on content for improved forgery resistance.
citing papers explorer
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Are Watermarked Images Editable? SafeMark for Watermark-Preserving Text-Guided Image Editing
SafeMark integrates a thresholded watermark-decoding loss into diffusion editors to enable text-guided edits that preserve embedded watermarks with high bit accuracy.
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"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking
W-IR is the first watermarking framework to combine certified robustness via randomized smoothing in pixel and coordinate spaces with identity leakage mitigation via residual information loss minimization.
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Dual-Guard: Dual-Channel Latent Watermarking for Provenance and Tamper Localization in Diffusion Images
Dual-Guard embeds complementary watermarks in diffusion image generation to verify provenance and localize tampering with low error rates on a 2400-sample benchmark under reprompting and editing attacks.
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Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
Major watermarking benchmarks omit cross-lingual, cultural, and demographic reporting, creating a pluralistic evaluation gap that current governance mandates ignore.
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Authenticated Contradictions from Desynchronized Provenance and Watermarking
C2PA manifests and AI watermarks can independently validate contradictory claims on the same asset, and a cross-layer audit protocol resolves this with 100% accuracy on 3500 images.
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SEAL: Semantic Aware Image Watermarking
SEAL uses semantic embeddings and locality-sensitive hashing to create distortion-free, database-free watermarks for generative images that are conditioned on content for improved forgery resistance.