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.

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