LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation

While Image-to-Video models unlock new creative possibil-ities, they also raise risks of unauthorized adaptation, deepfakes, andscenario manipulation, making robust authentication and traceability so-lutions urgent for image owners. While image watermarking offers a solu-tion, existing methods expose a critical vulnerability: due to the inherentsemantic drift of generated videos, images watermarks survive only in ini-tial frames. This allows attackers to easily evade detection via temporalcropping—discarding early frames to obtain watermark-free derivativevideos. Consequently, in an era of pervasive I2V models, long-term ro-bustness is imperative for image watermarking. Since simply increasingforward embedding strength fails against severe generative divergence,we introduce LoT-Pass, a watermarking framework built on a new in-sight: only one-directional robustness enhancement is hard to achievelong-term robustness. We propose a two-way strategy: (1) expand therobustness distance by training under simulated temporal evolution inI2V generation, and (2) recover unextractable frames by reversing themthrough a temporal inversion module. This shift, from merely resistingdistortions to actively rewinding the video to a watermark-friendly state,enables LoT-Pass to maintain extractability even under temporal drift.Experiments on mainstream open-source and commercial I2V modelsdemonstrate that LoT-Pass achieves stronger long-term robustness whilepreserving imperceptibility, offering a new paradigm for image copyrightprotection in the era of widespread I2V adoption. Code