Universal Perturbation-based Secret Key-Controlled Data Hiding
Donghua Wang Wen Yao Tingsong Jiang Xiaoqian Chen
Deep neural networks (DNNs) are demonstrated to be vulnerable to universal
perturbation, a single quasi-perceptible perturbation that can deceive the DNN
on most images. However, the previous works are focused on using universal
perturbation to perform adversarial attacks, while the potential usability of
universal perturbation as data carriers in data hiding is less explored,
especially for the key-controlled data hiding method. In this paper, we propose
a novel universal perturbation-based secret key-controlled data-hiding method,
realizing data hiding with a single universal perturbation and data decoding
with the secret key-controlled decoder. Specifically, we optimize a single
universal perturbation, which serves as a data carrier that can hide multiple
secret images and be added to most cover images. Then, we devise a secret
key-controlled decoder to extract different secret images from the single
container image constructed by the universal perturbation by using different
secret keys. Moreover, a suppress loss function is proposed to prevent the
secret image from leakage. Furthermore, we adopt a robust module to boost the
decoder's capability against corruption. Finally, A co-joint optimization
strategy is proposed to find the optimal universal perturbation and decoder.
Extensive experiments are conducted on different datasets to demonstrate the
effectiveness of the proposed method. Additionally, the physical test performed
on platforms (e.g., WeChat and Twitter) verifies the usability of the proposed
method in practice.