CN117011766B - Artificial intelligence detection method and system based on intra-frame differentiation - Google Patents
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Abstract
The invention provides an artificial intelligent detection method and system based on intra-frame differencing, which realizes the accurate detection of AI behaviors by carrying out deep learning model training on differentiated data in a video frame, wherein the differentiated data in the frame is obtained through intra-frame network, the intra-frame network adopts a multi-level convolution layer iteration difference value to extract intra-frame features, and Euclidean distance and contrast loss function between the intra-frame features are calculated to obtain an image difference value, thereby overcoming the problems of limited accuracy and inconsistent effects on dynamic and static targets in the prior art, and realizing the quick and accurate detection.
Description
Technical Field
The application relates to the technical field of network security, in particular to an artificial intelligent detection method and system based on intra-frame differentiation.
Background
With the rapid development of artificial intelligence technology, AI is increasingly widely used in the fields of video analysis, security monitoring, intelligent transportation and the like. However, since the AI may have misuse, etc. when performing a task, it is particularly important to accurately detect the AI.
In the prior art, the detection of AI is mainly realized by image processing and computer vision technology, however, the methods have limited accuracy and inconsistent effects on dynamic and static targets.
Disclosure of Invention
The invention aims to provide an artificial intelligence detection method and system based on intra-frame differentiation, which realize the accurate detection of AI by training a deep learning model. The system comprises a video acquisition module, a differential data processing module, a deep learning module and a detection output module.
In a first aspect, the present application provides an artificial intelligence detection method based on intra-frame differentiation, the method comprising:
step one, acquiring video data through video acquisition equipment, and respectively inputting the video data into the subsequent step two and step four;
inputting the received video data into an intra-frame network, extracting the intra-frame characteristics of each frame of image, and calculating image differentiation data between adjacent frames according to the intra-frame characteristics;
the intra-frame network in the second step comprises a plurality of convolution layers, wherein after the first-stage convolution layer receives video data, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to the next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
training a deep learning model by utilizing image differentiation data between the adjacent frames;
and step four, inputting the received video data into the trained deep learning model, detecting in real time, and outputting alarm information if AI behaviors are detected.
In a second aspect, the present application provides an artificial intelligence detection system based on intra-frame differentiation, the system comprising: the device comprises a video acquisition module, a differential data processing module, a deep learning module and a detection output module;
the video acquisition module is used for acquiring video data through video acquisition equipment and respectively inputting the video data into the subsequent differential data processing module and the detection output module;
the differential data processing module is used for inputting the received video data into an intra-frame network, extracting the intra-frame characteristic of each frame of image and calculating image differential data between adjacent frames according to the intra-frame characteristic;
the intra-frame network comprises a plurality of convolution layers, wherein after video data are received by a first-stage convolution layer, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to a next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
the deep learning module is used for training a deep learning model by utilizing image differentiation data between the adjacent frames;
and the detection output module is used for inputting the received video data into the trained deep learning model to perform real-time detection, and outputting alarm information if AI behaviors are detected.
In a third aspect, the present application provides an intra-frame differentiation-based artificial intelligence detection system comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method according to any one of the four possible aspects of the first aspect according to instructions in the program code.
In a fourth aspect, the present application provides a computer readable storage medium for storing program code for performing the method of any one of the four possible aspects of the first aspect.
Advantageous effects
The invention provides an artificial intelligent detection method and system based on intra-frame differencing, which realizes the accurate detection of AI behaviors by carrying out deep learning model training on differentiated data in a video frame, wherein the differentiated data in the frame is obtained through intra-frame network, the intra-frame network adopts a multi-level convolution layer iteration difference value to extract intra-frame features, and Euclidean distance and contrast loss function between the intra-frame features are calculated to obtain an image difference value, thereby overcoming the problems of limited accuracy and inconsistent effects on dynamic and static targets in the prior art, and realizing the quick and accurate detection.
The invention has the advantages that:
high accuracy: through training and application of the deep learning model, the AI can be accurately detected, and the accuracy of the traditional image processing and computer vision methods is improved.
Effective for both dynamic and static targets: the system can effectively detect both dynamic and static targets by processing intra-frame differentiated data.
Real-time performance: the system detects video data in real time, can discover and output AI behaviors in time, and has practical application value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of an artificial intelligence detection method based on intra-frame differentiation according to the present invention;
FIG. 2 is a block diagram of an artificial intelligence detection system based on intra-frame differentiation according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
FIG. 1 is a general flow chart of an intra-frame differentiation-based artificial intelligence detection method provided herein, the method comprising:
step one, video data are collected through video collecting equipment such as cameras, monitoring equipment and the like, and the video data are respectively input into the following step two and step four;
inputting the received video data into an intra-frame network, extracting the intra-frame characteristic of each frame of image, and calculating image differentiation data between adjacent frames according to the intra-frame characteristic so as to highlight the behavior characteristic of AI;
the intra-frame network in the second step comprises a plurality of convolution layers, wherein after the first-stage convolution layer receives video data, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to the next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
training a deep learning model by utilizing image differentiation data between the adjacent frames; model training employs Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) to achieve accurate identification of AI behavior.
And step four, inputting the received video data into the trained deep learning model, detecting in real time, and outputting alarm information if AI behaviors are detected.
On the basis of the above embodiments, the training process of the deep learning model can be further optimized. In the model training process, technologies such as transfer learning and the like are adopted, a pre-trained model is used as a basic network, and the application scene of the method is adapted in a fine adjustment mode and the like.
In addition, advanced technologies such as an attention mechanism and the like can be introduced, and the identification accuracy of the model on the AI behavior is improved. And the attention mechanism is used for carrying out fine recognition on the key area of the target object and improving the capturing capability of texture detail information and the noise suppression capability in the image.
The attention mechanism mainly comprises the following steps:
calculating an input representation vector: first, the input information is represented as a vector or matrix. This vector or matrix may be calculated by the neural network layer or may be pre-processed.
Calculating attention weight: next, the weights of the different parts of the input are calculated by one attention mechanism. This weight is typically calculated by comparing the correlation of different parts of the input with the information of current interest.
Calculating a weighted sum: and according to the calculated weight, carrying out weighted sum calculation on different input parts to obtain a final output. This output is typically a vector or matrix representing a weighted sum of the different parts of the input information.
Differentiable attention mechanisms: finally, the attention weight is combined with the input vector or matrix by a differentiable attention mechanism to obtain the final output. This attention mechanism may be a differentiable mechanism such as dot product, additive, multi-layer perceptron, etc., so that the overall calculation process can be optimized by a back propagation algorithm.
The real-time performance of the system can be further optimized. The system is deployed on a plurality of computing nodes by adopting the technologies of distributed computing and the like, so that parallel processing and real-time detection are realized. In addition, the training and application process of the deep learning model can be accelerated by using high-performance computing equipment such as a GPU and the like, and the real-time performance of the system is improved.
In some preferred embodiments, the calculating the euclidean distance between the intra-frame features of the current frame and the intra-frame features of the neighboring frames includes calculating the euclidean distance between the intra-frame features of the current frame and the intra-frame features of the previous and next frames, respectively.
In some preferred embodiments, the training of the deep learning model minimizes the entropy loss function by a reverse propagation mode, avoids oversaturation, and indicates that the training of the deep learning model is completed when the accuracy of the deep learning model meets the requirement of a threshold.
In some preferred embodiments, the image differentiation data comprises: pixel difference, texture change, shape change, motion change, color change, or a combination thereof.
The intra-frame differentiated data includes the following:
pixel difference: the pixel difference between adjacent frames can be obtained by calculating the difference in pixel values. For example, the absolute or relative difference of pixel values may be used to measure the image difference between adjacent frames.
Texture change: in video, there may be differences in texture of different regions, which can be used to distinguish between different objects or scenes. Such differentiated information can be obtained by calculating the variation of texture between adjacent frames.
Shape change: in video, the shape of the object may change, for example, the size, shape, and position of the object. Such differentiated information can be obtained by calculating the shape change between adjacent frames.
Motion change: in video, motion variation of objects is one of the common differentiating features. Such differentiated information may be obtained by calculating changes in motion between adjacent frames, such as velocity, direction, acceleration, etc. of the object.
Color change: in video, color variation is also one of the common differentiating features. Such differentiated information may be obtained by calculating the change in color between adjacent frames, such as saturation and brightness of the color, etc.
The above is some common intra-frame differential data, and the differential information in the video can be extracted through the data, so as to accurately identify and detect artificial intelligent behaviors.
FIG. 2 is a block diagram of an intra-frame differentiation-based artificial intelligence detection system provided herein, the system comprising: the device comprises a video acquisition module, a differential data processing module, a deep learning module and a detection output module;
the video acquisition module is used for acquiring video data through video acquisition equipment and respectively inputting the video data into the subsequent differential data processing module and the detection output module;
the differential data processing module is used for inputting the received video data into an intra-frame network, extracting the intra-frame characteristic of each frame of image and calculating image differential data between adjacent frames according to the intra-frame characteristic;
the intra-frame network comprises a plurality of convolution layers, wherein after video data are received by a first-stage convolution layer, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to a next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
the deep learning module is used for training a deep learning model by utilizing image differentiation data between the adjacent frames;
and the detection output module is used for inputting the received video data into the trained deep learning model to perform real-time detection, and outputting alarm information if AI behaviors are detected.
The application provides an artificial intelligence detection system based on intra-frame differentiation, the system comprising: the system includes a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method according to any of the embodiments of the first aspect according to instructions in the program code.
The present application provides a computer readable storage medium for storing program code for performing the method of any one of the embodiments of the first aspect.
In a specific implementation, the present invention also provides a computer storage medium, where the computer storage medium may store a program, where the program may include some or all of the steps in the various embodiments of the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
The same or similar parts between the various embodiments of the present description are referred to each other. In particular, for the embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference should be made to the description of the method embodiments for the matters.
The embodiments of the present invention described above do not limit the scope of the present invention.
Claims (7)
1. An artificial intelligence detection method based on intra-frame differentiation, which is characterized by comprising the following steps:
step one, acquiring video data through video acquisition equipment, and respectively inputting the video data into the subsequent step two and step four;
inputting the received video data into an intra-frame network, extracting the intra-frame characteristics of each frame of image, and calculating image differentiation data between adjacent frames according to the intra-frame characteristics;
the intra-frame network in the second step comprises a plurality of convolution layers, wherein after the first-stage convolution layer receives video data, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to the next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
training a deep learning model by utilizing image differentiation data between the adjacent frames;
and step four, inputting the received video data into the trained deep learning model, detecting in real time, and outputting alarm information if AI behaviors are detected.
2. The method according to claim 1, characterized in that: the calculating the euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames comprises respectively calculating the euclidean distance between the intra-frame features of the current frame and the intra-frame features of the previous frame and the next frame.
3. The method according to claim 1, characterized in that: when the deep learning model is trained, the entropy loss function is minimized through a reverse propagation mode, supersaturation is avoided, and when the precision of the deep learning model meets the requirement of a threshold value, the deep learning model is trained.
4. A method according to claim 3, characterized in that: the image differentiation data includes: pixel difference, texture change, shape change, motion change, color change, or a combination thereof.
5. An artificial intelligence detection system based on intra-frame differentiation, the system comprising: the device comprises a video acquisition module, a differential data processing module, a deep learning module and a detection output module;
the video acquisition module is used for acquiring video data through video acquisition equipment and respectively inputting the video data into the subsequent differential data processing module and the detection output module;
the differential data processing module is used for inputting the received video data into an intra-frame network, extracting the intra-frame characteristic of each frame of image and calculating image differential data between adjacent frames according to the intra-frame characteristic;
the intra-frame network comprises a plurality of convolution layers, wherein after video data are received by a first-stage convolution layer, convolution operation is carried out to obtain a first convolution value, a difference value between the first convolution value and the video data is calculated, a first difference value is output, and the first difference value is transmitted to a next-stage convolution layer; the next-stage convolution layer receives the input first difference value, carries out convolution operation to obtain a second convolution value, calculates the difference value between the second convolution value and the first difference value, outputs a second difference value, and transmits the second difference value to the next-stage convolution layer; repeating the operation until the video data is transmitted to a final-stage convolution layer, performing convolution operation to obtain a final-stage convolution value, calculating the sum of the final-stage convolution value and the video data, and outputting the intra-frame characteristics of the video data;
the calculating the image differentiation data between adjacent frames according to the intra-frame features comprises the following steps: calculating Euclidean distance between the intra-frame features of the current frame and the intra-frame features of the adjacent frames, and then calculating a contrast loss function between the current frame and the adjacent frames according to the Euclidean distance to obtain an image difference value between the adjacent frames;
the deep learning module is used for training a deep learning model by utilizing image differentiation data between the adjacent frames;
and the detection output module is used for inputting the received video data into the trained deep learning model to perform real-time detection, and outputting alarm information if AI behaviors are detected.
6. An artificial intelligence detection system based on intra-frame differentiation, the system comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method according to any of the claims 1-4 according to instructions in the program code.
7. A computer readable storage medium, characterized in that the computer readable storage medium is for storing a program code for performing a method implementing any of claims 1-4.
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