Self adaption threshold-based image tampering detecting and positioning method

Abstract

The invention discloses a self adaption threshold-based image tampering detecting and positioning method which is based on mode noise and takes image content into account. The method comprises the following steps: noise residual errors of an image to be detected are extracted; the image to be detected, the noise residual errors of the image to be detected, and a reference mode noise of a source camera of the image to be detected are subjected to non-overlapping partitioning operation; correlation between the noise residual errors of the image to be detected and the reference mode noise of the source camera of the image to be detected is calculated in a partition by partition manner, determination is made according to a texture complexity selection threshold value of the corresponding image to be detected, and negative influence exerted on a detection result by texture complexity can be removed; based on a method of roughly determining a tampering position via the non-overlapping partitioning operation, correlation matching operation is performed via a rapid zero-mean value normalization cross correlation algorithm, tampering detecting and positioning efficiency of the method disclosed in the invention can be greatly improved, and an aim of accurately positioning the tampering can be attained.

Description

technical field [0001] The invention relates to an image forensics technology, in particular to an image tampering detection and positioning method using an adaptive threshold. Background technique [0002] With the widespread popularization of image acquisition devices such as professional cameras, consumer cameras, and smart phones with high-definition camera functions, especially the popularity of smart phones in recent years, the whole people has entered the "picture reading era". In order to meet people's requirements for image editing, a variety of powerful and easy-to-operate image processing software emerged as the times require, but this also caused a large number of tampered images to flood the Internet, news and other mass media, which brought a serious crisis of trust to the society. As an important information carrier, digital image has become a research hotspot in the field of digital image forensics how to ensure its authenticity in the process of storage, tra...

AI Technical Summary

Problems solved by technology

This method of tampering forensics has the following problems: 1) In practical applications, since the noise residuals of the image captured by the camera and the noise residual of the image to be tested are relatively small, they are easily affected by unfavorable factors such as image texture, thus causing tamper detection and the positioning effect is not ideal; 2) The efficiency of the fixed threshold sliding window method based on the correlation coefficient is extremely low, which leads to the low efficiency of the tampering evidence collection method

Although these methods can improve the quality of the noise residual of the image to be tested, so as to effectively solve the influence of unfavorable factors such as texture details and interference noise, but the algorithm complexity is high, the extraction method is too cumbersome, and there may be problems in the extraction process. Introduce new random noise, the method noise

The new random noise may have little effect on the source identification of the camera device. At this time, the correlation of the overall image is calculated without considering local factors. However, when image tampering is detected, it is usually block detection, and the new random noise is bound to affect the local block. Correlation match

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