Image steganography detection method based on Gabor filtering and convolutional neural network

Abstract

The invention provides an image steganography detection method based on Gabor filtering and a convolutional neural network, and belongs to the technical field of information hiding, and is characterized by comprising the following steps: selecting a carrier image and a secret-carrying image to generate a sample image; extracting steganography detection characteristics of the sample image; the steganalysis features and the class labels of the sample images are trained through an integrated classifier to obtain a steganalysis detector; and after steganography detection characteristics of the to-be-detected image are extracted, the steganography detection characteristics are input into the steganography detector for image steganography detection. Image filtering is performed by using a filterto construct a plurality of deep convolutional neural networks to carry out steganography detection feature learning, and the extraction of diversified learning type steganography detection characteristics is realized. Meanwhile, according to the method, the filtering coefficient is used for carrying out structural steganography detection feature extraction. Finally, the learning type steganography detection feature and the structural steganography detection feature are combined to serve as steganography detection features, steganography detection is carried out through an integrated classifier, and the steganography detection method remarkably reduces the detection error rate of image self-adaptive steganography.

Description

technical field [0001] The invention belongs to the technical field of information hiding, and in particular relates to an image steganographic detection method based on Gabor filtering and a convolutional neural network. Background technique [0002] Information hiding technology refers to the use of the insensitivity of human senses and the redundancy of the signal itself to embed information into the host signal (such as image, audio, video or text files), and detect or extract hidden information when necessary. technology. Information hiding technology mainly includes digital steganography, digital watermarking technology, visible cryptography, protocol steganography and so on. Digital steganography is a technology that conceals the existence of secret information by embedding secret information in redundant data of digital images, audio, video and other media. Digital steganalysis is the reverse technology of digital steganography, which is mainly dedicated to detecti...

AI Technical Summary

Problems solved by technology

[0008] 1. Although GFR is currently the steganographic detection feature with the lowest detection error rate, compared with the steganographic detection method based on deep learning, the detection error rate is still relatively high;

[0009] 2. The steganographic detection method based on the deep convolutional neural network proposed by Fridrich et al. and Zeng et al. has the problem that the filter types of the image processing layer are not rich enough;

[0010] 3. The existing technology has not yet used deep learning to realize the automatic learning and extraction of steganographic detection features;

[0011] 4. The complementarity between the stereotyped steganographic detection features and the learned steganographic detection features needs to be studied

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