This paper aims to improve privacy-preserving visual recognition, an
increasingly demanded feature in smart camera applications, by formulating a
unique adversarial training framework. The proposed framework explicitly learns
a degradation transform for the original video inputs, in order to optimize the
trade-off between target task performance and the associated privacy budgets on
the degraded video. A notable challenge is that the privacy budget, often
defined and measured in task-driven contexts, cannot be reliably indicated
using any single model performance, because a strong protection of privacy has
to sustain against any possible model that tries to hack privacy information.
Such an uncommon situation has motivated us to propose two strategies, i.e.,
budget model restarting and ensemble, to enhance the generalization of the
learned degradation on protecting privacy against unseen hacker models. Novel
training strategies, evaluation protocols, and result visualization methods
have been designed accordingly. Two experiments on privacy-preserving action
recognition, with privacy budgets defined in various ways, manifest the
compelling effectiveness of the proposed framework in simultaneously
maintaining high target task (action recognition) performance while suppressing
the privacy breach risk.

本文提出通过对图像进行优化来平衡隐私预算和目标任务分析性能的对抗训练框架，并且使用预算模型重启和集成策略来提高对新的黑客模型的保护性。通过使用两个隐私保护行动识别中的实验，该框架在同时维持高目标任务（行动识别）性能之余，成功抑制了隐私侵犯的风险。