AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under the exact-model and decision-only attacks evaluated in this work.
This work presents the first systematic robustness benchmark for local watermark robustness across 55 image transformations, and finds that signal distortions are often tolerated by the strongest methods, while geometric misalignment and generative local edits can completely impair payload recovery.
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