JPEG image double-compression automatic detection method

Abstract

The invention discloses a JPEG image double-compression automatic detection method, and the method comprises the steps: carrying out the preprocessing of a JPEG image; extracting a first effective number feature of a DCT coefficient and adjacent coefficient difference features of the JPEG image based on the Markov model, carrying out the fusion of the features, and carrying out the feature dimension reduction; and training and recognizing a support vector machine. The fusion features can be effectively used for various compression conditions (besides the common compression conditions, also comprising a condition that two compression qualities are equal and the second compression quality is 95). Moreover, after the extraction of the fusion features, the dimension reduction of a feature matrix is carried out, and the matrix after dimension reduction is taken as a feature vector for subsequent operation. The method reduces the calculation complexity, improves the algorithm efficiency, and is easy to use in reality.

Description

technical field [0001] The invention belongs to the technical fields of information security, pattern recognition and digital image processing, and in particular relates to a JPEG image double-compression automatic detection method. [0002] technical background [0003] With the continuous development of image editing software, as long as people master some basic skills, they can tamper with the image "seamlessly", which is difficult to distinguish with the naked eye. These easy-to-use software bring convenience to people's life, but also usher in new challenges for digital forensics technology. In the context of the Internet era, images are easily tampered with maliciously by criminals, so it is necessary to determine the authenticity and integrity of a graphic. JPEG standard compression format has been widely used in digital cameras and digital image processing software, therefore, research work related to JPEG has received more and more attention. After the image has be...

AI Technical Summary

Problems solved by technology

[0006] The above research results have achieved certain detection results, but there are still some areas that can be improved.

One is that most detection methods have poor detection effect for special cases such as diagonal or second compression ratio of 95, and no suitable features have been found to detect all image compression situations with high precision

The second is that the feature dimension is too high, resulting in data redundancy, which affects the detection rate and detection rate

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