对抗性攻击用于善：视觉内容生命周期主动保护综述
原标题：Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
AI 摘要
这篇综述论文探讨了“对抗性攻击用于善”的保护范式，即在视觉内容生命周期中，数据所有者、创作者、平台或审计者应用扰动和结构化信号来干扰未经授权的自动化或支持事后问责。论文识别了五个独立研究社区，分别针对隐私过滤、不可学习示例、生成式防护、对抗性验证码和溯源机制。评估发现大多数保护措施仍主要针对静态或弱自适应对手进行验证，缺乏受控基准之外的证据。
正文节选
Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle Abstract 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 adversarial attacks for goo