Hiding Visual Information via Obfuscating Adversarial Perturbations
This addresses privacy concerns for users of visual data by preventing misuse, though it is incremental as it builds on existing adversarial perturbation methods.
The paper tackles the problem of protecting visual privacy by hiding visual information in data while maintaining model performance, achieving effective obfuscation with minimal impact on tasks like recognition and classification.
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.