Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
Once visual content enters an AI pipeline, its owner often retains little technical control over how it is used. Legal and regulatory remedies can address misuse, but many technical interventions must be applied earlier, when content is released or accessed. This survey examines the protective paradigm that has grown around this intervention point, which we call \emph{adversarial attacks for good}. Perturbations and structured signals long studied as attacks on learned models are instead applied by data owners, creators, platforms, or auditors to disrupt unauthorized automation or support later accountability. Five research communities have arrived at this inversion largely independently,...
| Authors | Jiaming Zhang, Boyang Chen, Zherui Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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