Proactive image protection shows changing robustness under repeated edits
PREEMPT: Proactive Robustness Evaluation under Editing for Media Protection Techniques
Computer Vision and Pattern Recognition
Summary
Images can be protected by embedding signals that stop unauthorized changes or misuse. The paper introduces PREEMPT, a way to test how well these protections hold up when images are edited multiple times, not just once. The authors found that protections can fail in different ways depending on the method used and that the first edit influences future changes a lot. Their method helps understand how image protections degrade over time with realistic editing.
What this means in practice
- •For digital content platforms: Assess the durability of image protection methods under multiple realistic edits to enhance content integrity on social media or publishing sites.
- •For graphic software developers: Design tools that evaluate image protection resilience during multi-step editing processes to better support content creators and rights holders.
Authors
Qiuyu Tang, Aparna Bharati
Abstract
Proactive image protection methods aim to defend visual content against unauthorized manipulation and generative misuse by embedding protective signals into images before release. Evaluating whether these protections remain effective under image editing is therefore critical. However, existing evaluations remain largely limited to one-shot edits, providing only a static view of robustness while overlooking its evolution under realistic multi-step editing workflows. To systematically characterize robustness dynamics under sequential editing across various protection paradigms, we introduce PREEMPT, a holistic evaluation framework. It evaluates protection robustness under both one-shot and sequential editing scenarios, covering image transformations, semantic edits, homogeneous and heterogeneous edit compositions. To enable consistent analysis across protection paradigms with different underlying objectives, PREEMPT normalizes task-specific effectiveness into protection survival trajectories and summarizes long-horizon robustness through trajectory-based diagnostics. Using this framework, we reveal that degradation behaviors remain hidden under conventional one-shot evaluation. Furthermore, perturbation- and watermark-based protection methods exhibit different failure modes, and the first semantic edit largely determines subsequent degradation trajectories. PREEMPT provides a standardized protocol for studying long-horizon robustness and offers new insights into the degradation dynamics of proactive image protection.