"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing
"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing
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DOI：
10.56553/popets-2023-0080
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Zhujun Xiao;Jenna Cryan;Yuanshun Yao;Yi Hong Gordon Cheo;Yuanchao Shu;S. Saroiu;Ben Y. Zhao;Haitao Zheng
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作者：
Zhujun Xiao;Jenna Cryan;Yuanshun Yao;Yi Hong Gordon Cheo;Yuanchao Shu;S. Saroiu;Ben Y. Zhao;Haitao Zheng
今天，面部编辑被广泛用于在专业和娱乐环境中改进/修改照片。然而，它也被用来修改（和转发）现有的网络欺凌照片。我们的工作考虑了一个重要的开放问题：“我们如何支持在社交平台上协同使用人脸编辑，同时防止他人进行不可接受的编辑和转发？”“这是一个挑战，因为正如我们的用户研究所显示的那样，用户对哪些编辑是（不可）接受的定义差异很大。社交平台部署的任何全局过滤策略都不太可能满足所有用户的需求，反而会阻碍照片编辑所实现的社交互动。相反，我们认为，面部编辑保护政策应该由社交平台根据个人用户的偏好来实施。当在线发布原始照片时，用户可以选择指定照片上允许的面部编辑（dis）类型。社交平台使用这些每张照片编辑策略来调节未来的照片上传，即，包含违反原始照片策略的修改的编辑照片被阻止或搁置以供用户批准。然而，实现这种个性化保护面临两个直接挑战：（1）如何准确识别照片中包含的特定修改（如果有的话）;以及（2）如何将编辑后的照片与其原始照片（以及编辑策略）相关联。我们表明，这些挑战可以通过结合高效的基于散列的图像搜索和可扩展的语义图像比较来解决，并建立一个原型保护器（Alethia），涵盖9种编辑类型。使用IRB批准的用户研究和数据驱动实验（839 K人脸照片）进行的评估表明，Alethia准确地识别出了违反用户政策的编辑照片，并为研究参与者带来了保护感。这证明了个性化人脸编辑保护的初步可行性。我们还讨论了当前的局限性和未来的方向，以推动概念向前发展。
Today, face editing is widely used to refine/alter photos in both professional and recreational settings. Yet it is also used to modify (and repost) existing online photos for cyberbullying. Our work considers an important open question: 'How can we support the collaborative use of face editing on social platforms while protecting against unacceptable edits and reposts by others?' This is challenging because, as our user study shows, users vary widely in their definition of what edits are (un)acceptable. Any global filter policy deployed by social platforms is unlikely to address the needs of all users, but hinders social interactions enabled by photo editing. Instead, we argue that face edit protection policies should be implemented by social platforms based on individual user preferences. When posting an original photo online, a user can choose to specify the types of face edits (dis)allowed on the photo. Social platforms use these per-photo edit policies to moderate future photo uploads, i.e., edited photos containing modifications that violate the original photo's policy are either blocked or shelved for user approval. Realizing this personalized protection, however, faces two immediate challenges: (1) how to accurately recognize specific modifications, if any, contained in a photo; and (2) how to associate an edited photo with its original photo (and thus the edit policy). We show that these challenges can be addressed by combining highly efficient hashing based image search and scalable semantic image comparison, and build a prototype protector (Alethia) covering nine edit types. Evaluations using IRB-approved user studies and data-driven experiments (on 839K face photos) show that Alethia accurately recognizes edited photos that violate user policies and induces a feeling of protection to study participants. This demonstrates the initial feasibility of personalized face edit protection. We also discuss current limitations and future directions to push the concept forward.