US 2023/0066458 A1 · STEGANOGRAPHY METHOD

Abstract

The present application relates to a steganography method and a steganography apparatus using the same. According to the steganography method of the present application and the steganography apparatus using the same, they can simplify a data refinement process compared to the supervised learning method as AI learns a large amount of data related to steganography encoding and decoding by itself, can generate a high-quality stego video close to the cover video to improve the imperceptibility of hidden information as the generator and discriminator conduct mutual learning, can hide the message in the images and/or sounds according to the learning method to secure a high-capacity cache, and can secure robustness against third-party detection, monitoring, and removal attacks when learning the detection and avoidance methods of physical and technical detection systems (monitoring equipment, wiretapping equipment and security equipment, etc.). In addition, according to the steganography method of the present application, it can simplify a data refinement process compared to the supervised learning method as AI learns a large amount of data related to steganography decoding by itself, and can recover the hidden message close to the original hidden message to improve perceptibility of hidden information as the generator and the discriminator conduct mutual learning.

Claims12

Dependent claims

A steganography method comprising:

a first generating neural network application step of embedding an original hidden message in an original cover video to generate a first stego video by applying a first generating neural network thereto;
a first discriminant correction step of comparing the first stego video generated in the first generating neural network application step with the original cover video two or more times by using a first discriminant neural network, and correcting it;
a first learning step of training the first generating neural network so that the first stego video generated in the first generating neural network application step is determined to be a real video in the first discriminant correction step;
a second learning step of training the first discriminant neural network so that when the original cover video is input in the first discriminant correction step, it is determined to be real, and when the video derived from the first generating neural network is input therein, it is determined to be fake;
and
a second stego video acquisition step of acquiring a second stego video by using a first generative adversarial neural network induced (adversarial training) through a mutual adversarial relationship between the first generating neural network and the first discriminant neural network.

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Dependencies are inferred from the claim text.

Steganography method

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Steganography method

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Abstract

Claims12