DeepBlur: A Simple and Effective Method for Natural Image Obfuscation
This addresses privacy concerns in social media and surveillance by offering a more effective and efficient image obfuscation method, though it appears incremental as it builds on existing generative models.
The paper tackles the problem of image obfuscation for privacy by introducing DeepBlur, a method that blurs images in the latent space of a pre-trained generative model, resulting in high-quality outputs that provide the strongest defense against re-identification attacks in most test cases.
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.