DeepBlur：一种简单有效的自然图像模糊方法
DeepBlur: A Simple and Effective Method for Natural Image Obfuscation
摘要：
由于社交媒体和监控系统的普及，以及面部识别软件的进步，人们对隐私的担忧越来越大。然而，已建立的图像混淆技术或者易受人类或深度学习模型的重新识别攻击，不足以保持图像保真度，或者计算量太大而不实用。为了解决这些问题，我们提出了DeepBlur，这是一种简单而有效的图像模糊方法，通过在无条件预训练的生成模型的潜在空间中进行模糊处理，该生成模型能够合成照片般逼真的面部图像。我们通过效率和图像质量将其与现有方法进行比较，并对最先进的深度学习模型和工业产品（例如，Face++，Microsoft Face服务）。实验表明，我们的方法产生高质量的输出，是大多数测试用例的最强防御。
英文摘要：
There is a growing privacy concern due to the popularity of social media and surveillance systems, along with advances in face recognition software. However, established image obfuscation techniques are either vulnerable to re-identification attacks by human or deep learning models, insufficient in preserving image fidelity, or too computationally intensive to be practical. To tackle these issues, we present DeepBlur, a simple yet effective method for image obfuscation by blurring in the latent space of an unconditionally pre-trained generative model that is able to synthesize photo-realistic facial images. We compare it with existing methods by efficiency and image quality, and evaluate against both state-of-the-art deep learning models and industrial products (e.g., Face++, Microsoft face service). Experiments show that our method produces high quality outputs and is the strongest defense for most test cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
