Sterilization of image steganography using self-supervised convolutional neural network.

ABSTRACT:

Background

With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks.

Methods

To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement

SUBMITTER: Liu J 

PROVIDER: S-EPMC11623220 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature