Visual Privacy Protection in Mobile Image Recognition Using Protective Perturbation

Deep neural networks (DNNs) have been widely adopted in mobile image recognition applications. Considering intellectual property and computation resources, the image recognition model is often deployed at the service provider end, which takes input images from the user's mobile device and accomplishes the recognition task. However, from the user's perspective, the input images could contain sensitive information that is subject to visual privacy concerns, and the user must protect the privacy while offloading them to the service provider. To address the visual privacy issue, we develop a protective perturbation generator at the user end, which adds perturbations to the input images to prevent privacy leakage. Meanwhile, the image recognition model still runs at the service provider end to recognize the protected images without the need of being re-trained. Our evaluations using the CIFAR-10 dataset and 8 image recognition models demonstrate effective visual privacy protection while maintaining high recognition accuracy. Also, the protective perturbation generator achieves premium timing performance suitable for real-time image recognition applications.