CN107609595B - Line cutting image detection method - Google Patents
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Abstract
The invention relates to a line cutting image detection method, which comprises two stages of training and testing; a training stage step: performing graying processing on the training set image, extracting LBP (local binary pattern) features and WLD (white line definition) features of the training set image one by one, performing feature selection on the extracted features by using a Kruskal-Wallis method, determining an optimal threshold value, and performing series fusion on the LBP features and the WLD features after feature selection to form a training feature set; a testing stage step: graying the test image according to the training stage steps, extracting LBP (local binary pattern) features and WLD (white line definition) features, performing feature selection on the extracted LBP features and WLD features by using an optimal threshold value determined in the training stage, and performing feature fusion to form a test feature set; and automatically searching an optimal parameter by the SVM classifier to judge whether the test image is subjected to line cutting operation. The method can be widely applied to the field of line cutting image detection, and the detection accuracy is improved.
Description
Technical Field
The invention relates to a line cutting image detection method.
Background
With the proliferation of inexpensive, portable image capture devices, almost everyone can record, store, and share digital images. Meanwhile, it becomes easier for a common user to tamper with the image by using different image editing software; moreover, it is very difficult or even impossible to distinguish the authenticity of the tampered image by the naked eye. Therefore, image forensics detection is a very active research direction in the field of information security. The line cutting method is an image scaling method which is widely used and keeps the content of an image unchanged; it achieves a preferential zoom performance, in particular a good compromise in zooming between the protection of important areas of the image and the preservation of the content as a whole. The line clipping method has been embedded into PhotoShop CS 6 and GIMP as an adaptive scaling method. However, line cropping methods are also used for the purpose of tampering with the image content. First, it may be used to correct a component of an image, which would be a fraud if the resulting picture was used for a competition; secondly, it may also be used intentionally to perform object deletion, which would change the semantics of the image. Therefore, it is worth studying to design a passive evidence-obtaining method to reveal those through line clipping methods.
In recent years, there have been some image detection methods related to line cropping; initially, forensic hashing was used to detect line-cut images, which effectively detected whether the image was scaled by a line-cut method; however, the forensic hash is usually established in the original image in advance, which is an active forensic method; and it may be removed by a tamperer causing a detection failure. In the blind forensics method, 324-dimensional markov features are extracted from candidate images to reveal line-cut images; the wavelet absolute moment is used for extracting the characteristics of the line cutting image; some use the packet decomposition method to extract the characteristic of the suspected image to carry on the recognition of the line cutting image; some methods extract features of the suspicious image based on energy bias and noise to identify whether the image is subjected to a line cropping operation.
Although these algorithms are relatively sophisticated, these methods all suffer from the same disadvantages. The inherent mechanism of the line clipping operation is not fully considered, nor is the visual distortion brought about by the line clipping operation further explored.
The invention content is as follows:
the invention provides a line cutting image detection method, which can detect whether images to be tested are subjected to line cutting operation in batch.
The technical scheme for solving the problems is divided into two stages, and the specific steps are as follows:
a training stage:
the method comprises the following steps: judging whether the training set image is a gray image or not; if not, graying the image;
step two: extracting LBP characteristics and WLD characteristics of the training set images one by one; LBP characteristics: using 1 as neighborhood radius, 8 neighborhoodsLBP coding with pixels representing the center pixel; thus generating a 28The WLD feature divides the image into 3 × 3 blocks each containing 8 directions, extracts an 8-dimensional histogram in each direction, then takes the entire image as one block, extracts an 8-dimensional histogram in each direction, and finally generates ((3 × 3) +1) × 8 × 8-640-dimensional features.
Step three: performing feature extraction on the features extracted in the second step by using a Kruskal-Wallis statistical method, and removing features which do not contribute to the final recognition rate;
step four: performing feature fusion on the LBP feature and the WLD feature obtained after the feature selection in the step three;
and (3) a testing stage:
the method comprises the following steps: carrying out graying processing on the test image according to the operation of the first step in the training stage;
step two: extracting LBP characteristics and WLD characteristics from the test image according to the method of the second training stage;
step three: performing feature selection operation on the extracted LBP feature and WLD feature according to a Kruskal-Wallis statistical method used in the third step of the training stage;
step four: performing fusion operation on the LBP characteristic and the WLD characteristic subjected to the characteristic selection operation;
step five: transmitting the feature set obtained in the step four and the feature set obtained in the step four in the training stage to an SVM classifier; the classifier can automatically find the optimal parameters to judge whether the image to be tested is subjected to line cutting operation.
The invention has the beneficial effects that: the invention relates to a line cutting image detection method, which comprises two stages: a training stage and a testing stage; and (3) carrying out feature selection on the features extracted in the training stage by a Kruskal-Wallis statistical method to form a training feature set, extracting a test feature set in the testing stage, transmitting the training feature set and the test feature set to an SVM classifier, and finally automatically judging whether the test image is subjected to line cutting operation by the classifier. The method fully considers the influence of line cutting on the image and improves the detection accuracy. Therefore, the method can be widely applied to the field of line cutting image detection.
Drawings
Fig. 1 is a general flowchart of a line-cutting image detection method according to the present invention.
Claims (2)
1. A line cutting image detection method is characterized by comprising the following specific steps:
a training stage:
a training stage comprises the following steps: judging whether the training set image is a gray image, if not, performing graying processing on the training set image;
a second step in the training stage: extracting LBP features and WLD features of the training set images one by one; LBP characteristics: taking 1 as a neighborhood radius, and expressing LBP coding of a central pixel by 8 neighborhood pixels; thus generating a 28The WLD feature is that the image is divided into 3 × 3 blocks, each block comprises 8 directions, 8-dimensional histograms are extracted in each direction, then the whole image is taken as one block, 8-dimensional histograms are extracted in each direction, and finally ((3 × 3) +1) × 8 × 8 is generated as 640-dimensional features;
a training stage comprises the following steps: respectively selecting the extracted LBP characteristics and WLD characteristics by using a Kruskal-Wallis statistical method; determining an optimal threshold value through a Kruskal-Wallis test, wherein the features smaller than the threshold value are left, and the features larger than the threshold value are automatically deleted;
step four: carrying out series fusion on the LBP characteristic and the WLD characteristic after characteristic selection to form a training characteristic set;
and (3) a testing stage:
the first step of the test stage: carrying out graying processing on the test image according to the first step of the training stage;
a second testing stage step: extracting LBP characteristics and WLD characteristics of the test image;
a third step of the test stage: performing feature selection operation on the LBP features and the WLD features extracted in the second step of the testing stage by using an optimal threshold determined by Kruskal-Wallis testing in the training stage, and removing features which do not contribute much to the recognition rate; fusing the LBP characteristic and the WLD characteristic after the characteristic selection operation to form a test characteristic set;
the fourth step of the test stage: transmitting the training feature set obtained in the fourth training stage step and the test feature set obtained in the third testing stage step to an SVM classifier as feature vectors; the SVM classifier automatically searches for optimal parameters to classify the feature set, so that whether the test image is subjected to line cutting operation or not is determined.
2. The line cropping image detection method of claim 1, wherein the extracted LBP feature and WLD feature are respectively subjected to feature dimension reduction by using Kruskal-Wallis method, so as to obtain an optimal threshold; this threshold is automatically optimized by the Kruskal-Wallis method, rather than being manually selected.
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| CN105488758A (en) * | 2015-11-30 | 2016-04-13 | 河北工业大学 | Image scaling method based on content awareness |
| CN105825183A (en) * | 2016-03-14 | 2016-08-03 | 合肥工业大学 | Face expression identification method based on partially shielded image |
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| CN105488758A (en) * | 2015-11-30 | 2016-04-13 | 河北工业大学 | Image scaling method based on content awareness |
| CN105825183A (en) * | 2016-03-14 | 2016-08-03 | 合肥工业大学 | Face expression identification method based on partially shielded image |
Non-Patent Citations (3)
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| 基于局部二元模式的快速红外人脸识别系统;谢志华;《红外与激光工程》;20131225;第42卷(第12期);第3191-3193页第1-3节 * |
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