Multitask Identity-Aware Image Steganography via Minimax Optimization
- URL: http://arxiv.org/abs/2107.05819v1
- Date: Tue, 13 Jul 2021 02:53:38 GMT
- Title: Multitask Identity-Aware Image Steganography via Minimax Optimization
- Authors: Jiabao Cui, Pengyi Zhang, Songyuan Li, Liangli Zheng, Cuizhu Bao,
Jupeng Xia, Xi Li
- Abstract summary: We propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images.
The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time.
In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method.
- Score: 9.062839197237807
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-capacity image steganography, aimed at concealing a secret image in a
cover image, is a technique to preserve sensitive data, e.g., faces and
fingerprints. Previous methods focus on the security during transmission and
subsequently run a risk of privacy leakage after the restoration of secret
images at the receiving end. To address this issue, we propose a framework,
called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct
recognition on container images without restoring secret images. The key issue
of the direct recognition is to preserve identity information of secret images
into container images and make container images look similar to cover images at
the same time. Thus, we introduce a simple content loss to preserve the
identity information, and design a minimax optimization to deal with the
contradictory aspects. We demonstrate that the robustness results can be
transferred across different cover datasets. In order to be flexible for the
secret image restoration in some cases, we incorporate an optional restoration
network into our method, providing a multitask framework. The experiments under
the multitask scenario show the effectiveness of our framework compared with
other visual information hiding methods and state-of-the-art high-capacity
image steganography methods.
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