Image enhancement detection method and system based on directional-consistency convolutional neural network

Abstract

The invention discloses an image enhancement detection method based on a directional-consistency convolutional neural network. The method comprises: a to-be-tested image is selected and the image is cut into one with a fixed size, wherein cutting is carried out by a central cutting way; the cut image is inputted into a pre-trained directional-consistency convolutional neural network, and probabilities of carrying out no image enhancement and carrying out image enhancement on the to-be-tested image are calculated; and the probabilities of carrying out no image enhancement and carrying out imageenhancement on the to-be-tested image are compared and then whether the to-be-tested image has been processed by image enhancement is determined. In addition, the invention also discloses an image enhancement detection system based on a directional-consistency convolutional neural network. The system is composed of an acquisition module, a calculation module and a determination template. According to the invention, evidence taking is carried out on the specific image enhancement operation, so that the high image detection rate is realized; and defects that the existing training method needs lots of time and efforts and overfitting is caused frequently can be overcome.

Description

technical field [0001] The present invention relates to the field of image forensics, and more specifically, to an image enhancement detection method and system based on a directionally consistent convolutional neural network. Background technique [0002] With the advent of the multimedia information age, the rapid popularization of digital equipment and image processing tools, on the one hand, has accelerated the progress of digital image technology and brought great convenience to people's lives; on the other hand, it has made digital images more vulnerable Tampering, which reduces the security and reliability of the image. Therefore, it is particularly important to determine the original source of the image and verify the authenticity and integrity of the image content. In recent years, image forensics technology has advanced by leaps and bounds, especially the image tampering detection technology based on convolutional neural network, which has greatly improved the acc...

AI Technical Summary

Problems solved by technology

However, most of the existing convolutional neural network-based image tampering detection techniques need to train a large number of parameters, which requires us to train through a huge image database. This method is time-consuming and laborious, and it is easy to cause over-fitting.

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