Airspace steganalysis method and system based on gradient network architecture search

Abstract

The invention provides an airspace steganalysis method and system based on gradient network architecture search, which solve the problems of poor performance and long network search time of the existing manual design network. The method comprises the following steps:firstly, defining a steganalysis network search architecture; determining a search architecture and candidate operations of each part of to-be-searched modules in a steganalysis network search architecture, fusing all candidate operations in a search space by using a Softmax function to construct a hyper-parameter network, and then training and optimizing the constructed hyper-parameter network by using a gradient descent method, selecting corresponding operation according to architecture parameters in the trained hyper-parameter network, and completing search and construction of the steganalysis network. Manual intervention in network design is reduced, the defect that the performance of the constructed steganalysis network is limited is overcome, and a network architecture search mode based on gradient updating reduces search time consumption.

Description

technical field [0001] The present invention relates to the technical field of image steganalysis, and more specifically, to a spatial domain steganalysis method and system based on gradient network architecture search. Background technique [0002] Steganalysis is an attack on steganography, the purpose is to detect the existence of secret information and destroy the secret communication. Steganalysis is the key technology to solve the problem of illegal use of steganography. Image steganalysis is a technique used to detect whether a digital image contains secret information embedded by steganography. Existing steganalysis methods can be divided into two categories, including traditional steganalysis methods based on artificially designed features and steganalysis methods based on deep learning. The traditional steganalysis method is represented by SRM. First, a large number of high-pass filter kernels are used to filter the image to obtain the corresponding residual image...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of poor performance of the existing artificially designed network and long time-consuming search for the network, the present invention proposes a spatial steganalysis method based on gradient network architecture search And the system can efficiently search in the preset search space to obtain a high-performance spatial image steganalysis network

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