An Information Hiding Detection Method Based on Local Learning

Abstract

The invention discloses an information hiding detection method based on local learning, which comprises: constructing a training sample database containing positive and negative samples; for any sample to be detected, searching for K positive and negative samples most similar to it in the sample database Yes, it constitutes a local training set; on the local training set, the training and learning of the classifier is carried out. During the learning process, the constraint of pairing positive and negative samples is added, and the optimal classifier is obtained by using the optimization algorithm; the obtained classifier is used to treat The detection samples are discriminated and classified to obtain a detection result of whether the samples to be detected have undergone information hiding. The present invention makes full use of local learning to better overcome the advantages of large intra-class changes, noise reduction and less need for prior knowledge, improves the effect of information hiding detection, and can be applied to an information hiding detection algorithm analysis system based on pattern recognition middle.

Description

technical field [0001] The invention relates to the field of image and video passive blind evidence collection, in particular to an information hiding detection method based on local learning for image and video information hiding detection. Background technique [0002] Information hiding uses the sensory redundancy of human sensory organs to digital signals to hide a set of secret information (authorization serial number, message or copyright information, etc.) into the carrier information, without affecting the sensory effect and use value of the host signal , making it difficult for possible attackers to judge whether the secret information exists, and even more difficult to intercept, thus ensuring the security of information transmission. Information hiding has become one of the basic communication methods to safely and reliably transmit political, military, economic and other information in the network environment. At present, there are sufficient evidences to prove ...

AI Technical Summary

Problems solved by technology

When using these classifiers, existing methods usually learn a global classification model on all training samples. The problem is that information hiding detection faces the difficulty of large intra-class scatter and small inter-class differences. The learned global classification model often Due to the low generalization performance due to the high complexity, many existing methods can only achieve good detection results under experimental conditions

In addition, the existing global learning methods usually need to train classifiers for each information hiding algorithm and each embedding rate separately. When detecting, it is necessary to know the embedding algorithm and embedding rate used by the sample to be tested in advance, which is very low in practicability.

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