  Growing leakage and misuse of visual information raise security and privacy
concerns, which promotes the development of information protection. Existing
adversarial perturbations-based methods mainly focus on the de-identification
against deep learning models. However, the inherent visual information of the
data has not been well protected. In this work, inspired by the Type-I
adversarial attack, we propose an adversarial visual information hiding method
to protect the visual privacy of data. Specifically, the method generates
obfuscating adversarial perturbations to obscure the visual information of the
data. Meanwhile, it maintains the hidden objectives to be correctly predicted
by models. In addition, our method does not modify the parameters of the
applied model, which makes it flexible for different scenarios. Experimental
results on the recognition and classification tasks demonstrate that the
proposed method can effectively hide visual information and hardly affect the
performances of models. The code is available in the supplementary material.
