Learnable Image Encryption
This addresses privacy concerns for researchers in surveillance applications, though it appears incremental as it builds on existing encryption ideas.
The paper tackles the privacy issue in collecting image datasets for surveillance by introducing a learnable image encryption scheme that allows networks to train on encrypted images while keeping them unintelligible to humans, validated on the CIFAR dataset.
The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues. In this paper, a simple learnable image encryption algorithm is proposed. Then, the proposed algorithm is validated with cifar dataset.