Image translation-based deniable encryption against model extraction attack

In cloud storage applications, data owners' original images are usually encrypted before being outsourced to the cloud for preserving data owners' privacy. However, in deep learning model-based image encryption methods, an adversary can conduct the model extraction attack to reveal the model parameters and thus restore the privacy information by obtaining numerous encrypted images. In this paper, we propose an image translation-based deniable encryption (ITDE) scheme to achieve encryption deniability and defend against model extraction attacks. Differing from traditional encryption methods in which encrypted images are visually meaningless, ITDE applies image translation to generate encrypted images in the form of human faces. Moreover, ITDE provides deniability for data owners to keep the encryption parameters private. To defend against model extraction attacks, the defense mechanism is introduced in our proposed ITDE to preserve deep learning models. Experimental results demonstrate the superiority of our proposed methods in terms of encryption deniability and privacy preservation.