Image stego-detection method on basis of deep learning

Abstract

The invention provides an image stego-detection method on the basis of deep learning, which comprises the following steps: filtering images which are correspondingly provided with steganography marks or reality marks in a training set by a high-pass filter so as to obtain a training set comprising steganography residue images and reality residue images; carrying out learning on the training set on a deep network model so as to obtain the trained deep network detection model; filtering an image to be detected by the high-pass filter so as to obtain a residue image to be detected; detecting the residue image to be detected on the deep network detection model so as to determine whether the residue image to be detected is a steganography image. The image stego-detection method provided by the invention can realize creation of a blind-detection model by automatic learning and can accurately identify the steganography image.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an image steganographic detection method based on deep learning. Background technique [0002] With the digitalization of media resources and the rapid development and application of the Internet in recent years, the acquisition of digital images and their exchange and transmission on the Internet have become very easy and common. Therefore, it also provides convenient conditions for information hiding based on digital images. Steganography is used to embed secret information into a normal carrier without changing the perceptual characteristics of the carrier, so as to realize the secret transmission of information. With the vigorous development of information hiding technology, a large number of steganographic methods have emerged. People can easily obtain and use a variety of steganography tools to exchange messages on the Internet. The misuse of steganography has brought inc...

AI Technical Summary

Problems solved by technology

In the current field of image steganalysis, there are many methods of feature design, such as [J.Fridrich and J.Kodovsky,"Rich Models for Steganalysis of Digital Images,"IEEE Trans.on Info.Forensics and Security, vol.7(3), pp.868-882, 2012] and [V.Holub and J.Fridrich, “Random projections of residuals for digital image steganalysis,” IEEE Transactions on Information Forensics and Security, vol.8, no. 12, pp.1996–2006, 2013.] The design and screening of these features, the setting of parameters is very dependent on specific data sets, and it takes a lot of time and energy, and has high requirements for human experience and knowledge

In practical applications, the complexity and diversity of real image data bring more challenges to feature design

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