论文标题
我的脸我的选择：隐私增强社交媒体匿名的深层效果
My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization
论文作者
论文摘要
最近，面部识别和识别算法的产生已成为有关道德AI的最具争议的话题。随着围绕数字身份的新政策，我们在假设的社交网络中介绍了三个面部访问模型，在该网络中，用户有能力仅在他们批准的照片中出现。我们的方法黯然失色的标记系统，并用定量不同的深击取代未经批准的面孔。此外，我们提出了针对此任务的新指标，在该任务中，随机生成深层味，并保证了差异。我们根据数据流的严格性来解释访问模型，并讨论每个模型对隐私，可用性和性能的影响。我们在面部描述数据集上评估我们的系统作为真实数据集，以及两个具有随机和平等类别分布的合成数据集。 MFMC在我们的结果中运行七个SOTA面部识别剂，将平均准确性降低了61％。最后，我们在结构，视觉和生成空间中广泛分析了相似性指标，深层生成器和数据集。支持设计选择并验证质量。
Recently, productization of face recognition and identification algorithms have become the most controversial topic about ethical AI. As new policies around digital identities are formed, we introduce three face access models in a hypothetical social network, where the user has the power to only appear in photos they approve. Our approach eclipses current tagging systems and replaces unapproved faces with quantitatively dissimilar deepfakes. In addition, we propose new metrics specific for this task, where the deepfake is generated at random with a guaranteed dissimilarity. We explain access models based on strictness of the data flow, and discuss impact of each model on privacy, usability, and performance. We evaluate our system on Facial Descriptor Dataset as the real dataset, and two synthetic datasets with random and equal class distributions. Running seven SOTA face recognizers on our results, MFMC reduces the average accuracy by 61%. Lastly, we extensively analyze similarity metrics, deepfake generators, and datasets in structural, visual, and generative spaces; supporting the design choices and verifying the quality.
