CN104182735A - Training optimization pornographic picture or video detection method based on convolutional neural network - Google Patents
Training optimization pornographic picture or video detection method based on convolutional neural network Download PDFInfo
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Abstract
The invention relates to a training optimization pornographic picture or video detection method based on a convolutional neural network. A final detection model is obtained through circuit training, classification detection accuracy can be improved to a largest extent, and deficiencies that a perfect complexion model and high detection accuracy are difficult to establish due to ambient light diversity and race diversity and the detection accuracy is always low due to external factors, such as human body gesture diversity, sheltering and the like can be overcome. When the method disclosed by the invention is used for picture data or video data to detect pictures or a video image sequence, only a classification result shows that the pictures or video image sequence is pornographic, a picture set or videos are defined as pornographic data, and a deficiency that a universal rule is difficult to establish for different pornographic videos is overcome.
Description
Technical field
The present invention relates to a kind of pornographic image detection method, more particularly, relate to a kind of pornographic image based on convolutional neural networks or video detecting method of training optimization.
Background technology
Along with the widespread use of internet, user also can run into flame when obtaining a large amount of useful informations, especially the most serious with pornographic image and pornographic video.The method that existing pornographic image or pornographic video detect is mainly to utilize the colour of skin to identify, but in actual applications, existing detection method exists the factor of following restriction Detection accuracy:
1) variation of ambient light, the diversity of ethnic group etc. make to be difficult to the complexion model of Erecting and improving and the accuracy rate of detection;
2) human body attitude diversity, the extraneous factor such as block and make the accuracy rate that detects always not high;
3) for different pornographic videos, be difficult to set up more common rule.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of impact that not only can adapt to various environment and attitude and the extraneous factor such as block is provided, and can set up more common detection rule, improve the pornographic image based on convolutional neural networks or the video detecting method of the training optimization of the accuracy rate detecting.
Technical scheme of the present invention is as follows:
Train the pornographic image based on convolutional neural networks or the video detecting method of optimization, default view data or video data; And utilization has been carried out convolutional neural networks and has been trained the Preliminary detection model obtaining to carry out classification and Detection for the first time; If there is wrong sample in classification and Detection result for the first time, to wrong sample, utilize convolutional neural networks to train for the second time, obtain transition detection model for the first time; And utilize transition detection model for the first time to carry out classification and Detection for the second time, if there is wrong sample in classification and Detection result for the second time, to wrong sample, utilize convolutional neural networks to train for the third time, obtain transition detection model for the second time, the above step that circulates, until wrong sample is less than default threshold values or does not exist in testing result, obtains final detection model; Utilize final detection model to carry out classification and Detection to view data to be measured or video data.
As preferably, default view data or video data comprise pornographic data and the normal data of presorting, using these two classes data as two scenes, as input data, offer convolutional neural networks and carry out learning classification, obtain judging whether view data or video data are pornographic and normal detection model.
As preferably, the process that training is optimized is: the transition detection model obtaining is carried out to class test to default view data or video data, the sample that the classification and Detection result view data different from the result of presorting or video data are decided to be to classification error.
As preferably, the sample of classification error is specially: the environment classification label that classification and Detection result is obtained is collected with the inconsistent sample image data of the environment classification label of presorting or video data, re-starts classification.
As preferably, the order of the network structure of convolutional neural networks is: input layer, some groups layer, full articulamentum, probability statistics layer.
As preferably, further, group's layer comprises convolutional layer, active coating successively.
As preferably, in convolutional layer, the size of core must be odd number, and is not more than the wide or high of this layer of input; Intermediate representation does not change during by convolutional layer wide and high, and whether port number does not limit and change; In active coating, active coating does not change wide, high, the port number that convolutional layer represents; After active coating is arranged on convolutional layer or full articulamentum; By the intermediate representation after full articulamentum, be 1 dimension, and after full articulamentum, cannot through group's layer, process again; Probability between real-valued the becoming [0,1] that probability statistics layer produces full articulamentum.
As preferably, probability statistics layer is exported the probability of each label, and selects the label of maximum probability as the type of present image.
As preferably, further, one deck of down-sampling layer and normalization layer or two-layer is set after active coating.
As preferably, down-sampling layer does not change the port number of intermediate representation, and the down-sampling layer size that is core to the drawdown ratio of image; Normalization layer does not change any size of intermediate representation.
Beneficial effect of the present invention is as follows:
Method of the present invention, through circuit training, obtain final detection model, can improve classification and Detection accuracy rate in maximum program ground, overcome the variation because of ambient light, the diversity of ethnic group etc. and make to be difficult to the complexion model of Erecting and improving and the accuracy rate of detection, and human body attitude diversity, the extraneous factor such as block and make the accuracy rate that detects not high deficiency always.
When method of the present invention is used for view data or video data, carry out the detection to picture, or the detection to sequence of video images, as long as classification results is pornographic, be that this image collection of definable or video are pornographic data, overcome for different pornographic videos and be difficult to set up more common regular deficiency.
Embodiment
Below in conjunction with embodiment, the present invention is described in further detail.
The invention provides a kind of pornographic image based on convolutional neural networks or video detecting method of training optimization, default view data or video data; And utilization has been carried out convolutional neural networks and has been trained the Preliminary detection model obtaining to carry out classification and Detection for the first time; If there is wrong sample in classification and Detection result for the first time, to wrong sample, utilize convolutional neural networks to train for the second time, obtain transition detection model for the first time; And utilize transition detection model for the first time to carry out classification and Detection for the second time, if there is wrong sample in classification and Detection result for the second time, to wrong sample, utilize convolutional neural networks to train for the third time, obtain transition detection model for the second time, the above step that circulates, until wrong sample is less than default threshold values or does not exist in testing result, be that error rate reaches tolerance interval or no longer changes, obtain final detection model; Utilize final detection model to carry out classification and Detection to view data to be measured or video data.
Detection model is mainly divided into two classes: pornographic data and normal data.Default view data or video data comprise pornographic data and the normal data of presorting, can be by manually carrying out category filter.Using these two classes data as two scenes, as input data, offer convolutional neural networks and carry out learning classification, obtain judging whether view data or video data are pornographic and normal detection model.
The process that training is optimized is: the transition detection model obtaining is carried out to class test to default view data or video data, the sample that the classification and Detection result view data different from the result of presorting or video data are decided to be to classification error, be specially: the environment classification label that classification and Detection result is obtained is collected with the inconsistent sample image data of the environment classification label of presorting or video data, re-start classification, adjust network structure.And the process of optimizing is the sample image after reclassifying is carried out to training study again, so repeat " training---adjusting network structure---test---is training " process until the error rate of classification and Detection result in tolerance interval.
The network of convolutional neural networks is multilayer, and in the present invention, the order of network structure is: input layer, some groups layer (at least one group of group's layer), full articulamentum, probability statistics layer.Group's layer comprises convolutional layer, active coating successively.According to implementing needs, further, one deck of down-sampling layer and normalization layer or two-layer is set after active coating.In convolutional layer, active coating, down-sampling layer, normalization layer, the core of each layer size and output size can carry out regulating arbitrarily, and each layer has an input and produce an output, and the output of every one deck is as the input of lower one deck.
In the present embodiment, the input of input layer size is Height x Weight x Channel, and wherein Weight, Height are the wide and high of input layer image, and Channel is the port number of input layer image; Because the present invention uses the hard-wired reason of GPU, Weight=Height; If input data are image, Channel value can only be 1 or 3.If video data, the port number that Channel number is image and total product of inputting frame number.
Convolutional layer:
1) size of core must be odd number, and is not more than the wide or high of this layer of input;
2) when intermediate representation is by convolutional layer, do not change widely and high, port number is variable can be constant; Can be any positive integer in theory, because the present invention uses the hard-wired reason of GPU, be 16 multiple here.
Active coating:
1) that active coating does not change that convolutional layer represents is wide, height or port number;
2) activation function that active coating is used includes but not limited to following type function:
f(x)＝1/(1+e
-x)；
F (x)=a*tanh (b*x), a, b is any non-zero real;
f(x)＝max(0,x)；
f(x)＝min(a,max(0,x))；
f(x)＝log(1+e
x)；
f(x)＝|x|；
f(x)＝x
2；
f(x)＝ax+b；
3) active coating is followed after convolutional layer or full articulamentum.
Down-sampling layer:
1) down-sampling layer does not change the port number of intermediate representation;
2) down-sampling layer is the size of core to the drawdown ratio of image: the down-sampling layer that core is m * n can cause intermediate representation to be reduced into last layer (1/m) * (1/n), m and n can be random natural number in theory, because the present invention uses the hard-wired reason of GPU, m=n.For example, 15 x 15 x 32, by after the down-sampling of 3 x 3, become 5 x 5 x 32; 15 x 15 x 32, by after the down-sampling of 5 x 5, become 3 x 3 x 32; But 15 x 15 x 32 can not carry out the down-sampling of 2 x 2, because 15 can not be divided exactly by 2; Be not, input size must be 2 inferior power, 16,32,64 etc., as long as input size guarantees to be sampled by all down-sampling layers.
Normalization layer:
1) normalization layer does not change any size of intermediate representation;
2) normalization layer not necessarily, adds normalization layer and conventionally can improve precision and increase calculated amount; Whether add normalization layer, see the precision of actual lifting after adding and the speed of loss.
General combination is: convolution---activates---down-sampling---normalization.
Following situation is special:
1) when interpolation normalization layer has but increased a lot of operand to precision improvement is less, cancel normalization layer, adopt following combination: convolution---activation---down-sampling;
2) in advance, effect is basic identical for normalization layer, adopts following combination: convolution---activates---normalization---down-sampling.
3) cancel down-sampling layer: convolution---activate; Or convolution---activation---normalization; Down-sampling essence is in order to increase robustness, has in passing the effect of the operand that reduces succeeding layer simultaneously; In a network, conventionally have which floor down-sampling, but not all " convolution---activate " all to follow down-sampling below.
Full articulamentum:
1) by the intermediate representation after full articulamentum, can become 1 dimension, be no longer 3 dimensions;
2) output of full articulamentum can be any;
3) once connect through complete, just cannot carry out convolution, down-sampling or normalization;
4) after full articulamentum, can connect active coating, or continue to connect full connection.
Probability statistics layer (SoftMax layer):
After being connected on full articulamentum, effect is the probability between real-valued the becoming [0,1] that full articulamentum is produced.Probability statistics layer is exported the probability of each label, and selects the label of maximum probability as the type of present image.That is,, by the input layer of view data to be detected or video data input neural network, the probability that obtains each label at last probability statistics layer is (interval [0,1] real-valued in), always have 2 types, i.e. 2 data, these 2 data and equal 1.Select the label of maximum probability as the type of this image.
In the present embodiment, it is as shown in table 1 that pornographic image detects the network structure of using:
Table 1: network structure table
| The number of plies | Type | Core size | Output size | Explain |
| 1 | Input layer | ? | 32x32x3 | ? |
| 2 | Convolutional layer | 5x5 | 32x32x32 | ? |
| 3 | Active coating | ? | 32x32x32 | f(x)＝|x| |
| 4 | Down-sampling layer | 2x2 | 16x16x32 | ? |
| 5 | Normalization layer | ? | 16x16x32 | Use local normalization |
| 6 | Convolutional layer | 5x5 | 16x16x16 | ? |
| 7 | Active coating | ? | 16x16x16 | f(x)＝x 2 |
| 8 | Down-sampling layer | 2x2 | 8x8x16 | ? |
| 9 | Normalization layer | ? | 8x8x16 | Use local normalization |
| 10 | Full articulamentum | ? | 7 data | ? |
| 11 | Probability statistics layer | ? | 7 data | ? |
In the present embodiment, it is as shown in table 2 that pornographic video detects the network structure of using:
Table 2: network structure table
| The number of plies | Type | Core size | Output size | Explain |
| 1 | Input layer | ? | 128x128x16 | ? |
| 2 | Convolutional layer | 7x7 | 122x122x64 | ? |
| 3 | Active coating | ? | 122x122x64 | f(x)＝max(0,x) |
| 4 | Normalization | 5x5 | 122x122x64 | Use local normalization |
| 5 | Down-sampling layer | 2x2 | 61x61x64 | ? |
| 6 | Full articulamentum | ? | 20 | ? |
| 7 | Full articulamentum | ? | 2 | ? |
| 8 | Probability statistics layer | ? | 2 data | ? |
While utilizing pornographic detection model of the present invention to carry out pornographic detection to view data to be detected, as long as detect picture result, be pornographic, judge that picture is as porny.
While utilizing pornographic detection model of the present invention to carry out pornographic detection to video data to be detected, first extract the image of video, in the present embodiment, mainly to take every 8 frames to get the mode of a frame to video, video data is converted into image sequence to be detected, as long as testing result is pornographic, judge that this video is as pornographic video.
Above-described embodiment is only for the present invention is described, and not as limitation of the invention.So long as according to technical spirit of the present invention, to above-described embodiment change, modification etc. all will drop in the scope of claim of the present invention.
Claims (10)
1. the pornographic image based on convolutional neural networks or a video detecting method of training optimization, is characterized in that, default view data or video data; And utilization has been carried out convolutional neural networks and has been trained the Preliminary detection model obtaining to carry out classification and Detection for the first time; If there is wrong sample in classification and Detection result for the first time, to wrong sample, utilize convolutional neural networks to train for the second time, obtain transition detection model for the first time; And utilize transition detection model for the first time to carry out classification and Detection for the second time, if there is wrong sample in classification and Detection result for the second time, to wrong sample, utilize convolutional neural networks to train for the third time, obtain transition detection model for the second time, the above step that circulates, until wrong sample is less than default threshold values or does not exist in testing result, obtains final detection model; Utilize final detection model to carry out classification and Detection to view data to be measured or video data.
2. training according to claim 1 is optimized the pornographic image based on convolutional neural networks or video detecting method, it is characterized in that, default view data or video data comprise pornographic data and the normal data of presorting, using these two classes data as two scenes, as input data, offer convolutional neural networks and carry out learning classification, obtain judging whether view data or video data are pornographic and normal detection model.
3. training according to claim 2 is optimized the pornographic image based on convolutional neural networks or video detecting method, it is characterized in that, the process that training is optimized is: the transition detection model obtaining is carried out to class test to default view data or video data, the sample that the classification and Detection result view data different from the result of presorting or video data are decided to be to classification error.
4. training according to claim 3 is optimized the pornographic image based on convolutional neural networks or video detecting method, it is characterized in that, the sample of classification error is specially: the environment classification label that classification and Detection result is obtained is collected with the inconsistent sample image data of the environment classification label of presorting or video data, re-starts classification.
5. training according to claim 1 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, the order of the network structure of convolutional neural networks is: input layer, some groups layer, full articulamentum, probability statistics layer.
6. training according to claim 5 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, further, group's layer comprises convolutional layer, active coating successively.
7. training according to claim 6 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, in convolutional layer, the size of core must be odd number, and is not more than the wide or high of this layer of input; Intermediate representation does not change during by convolutional layer wide and high, and whether port number does not limit and change; In active coating, active coating does not change wide, high, the port number that convolutional layer represents; After active coating is arranged on convolutional layer or full articulamentum; By the intermediate representation after full articulamentum, be 1 dimension, and after full articulamentum, cannot through group's layer, process again; Probability between real-valued the becoming [0,1] that probability statistics layer produces full articulamentum.
8. training according to claim 7 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, probability statistics layer is exported the probability of each label, and selects the label of maximum probability as the type of present image.
9. training according to claim 6 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, further, one deck of down-sampling layer and normalization layer or two-layer is set after active coating.
10. training according to claim 8 is optimized the pornographic image based on convolutional neural networks or video detecting method, is characterized in that, down-sampling layer does not change the port number of intermediate representation, and the down-sampling layer size that is core to the drawdown ratio of image; Normalization layer does not change any size of intermediate representation.
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| CN111199233A (en) * | 2019-12-30 | 2020-05-26 | 四川大学 | Improved deep learning pornographic image identification method |
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