Random sub-graph representation-based image steganography detection method for object in cloud environment
An object image and detection method technology, applied in the fields of computer vision and cryptography, can solve the problems affecting the calculation speed, high cost, and time-consuming of secret detection algorithms of face images, so as to shorten the time and structure of program testing. Simple, easy to achieve effects
Active Publication Date: 2016-11-16
BEIJING ELECTRONICS SCI & TECH INST
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Problems solved by technology
The main problem of the Secure Classifier protocol is a large number of encryption and decryption calculations and the lack of use of integral images to accelerate face detection, which seriously affects the calculation speed of face image stealth detection algorithms based on inadvertent transmission.
Experiments show that it takes a few minutes for a 24×24 detection window to be detected, and a 240×320 picture has about 150,000 detection windows, so it takes a lot of time to detect a 240×320 picture, so it takes a lot of time to detect a picture cost too much
In summary, the stealth detection algorithm for face images based on inadvertent transmission is not very ideal
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[0033] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.
[0034] Such as figure 1 with figure 2 as shown,
[0035] (1) Alice, the client, converts the input image into an image X with each pixel being 0-255;
[0036] (2) Alice creates 256 sub-images SM r , the pixels of these subimages are initialized to 0, and the rth subimage SM rThe weight of is Q[r]=r, where the value range of r is 0-255;
[0037] (3) For each pixel X[i,j] in the image X, Alice performs the following sub-process until X[i,j] is 0. Finally, the input image X is decomposed into 256 random subgraphs SM r ;
[0038] (31) Generate a random number z, the value range of z is X[i,j] / 2r The pixel value of point [i,j] is 1;
[0039] (32) reset X[i, j]=X[i, j]-z, then go to process (31), until X[i, j]=0;
[0040] (4) Alice puts the sub-image SM r Rearrange according to the order of the set of Q' to get SM' r , put the rearranged subimag...
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The invention proposes a random sub-graph representation-based image steganography detection method for an object in a cloud environment. The method can be implemented securely, and the privacy of pictures of users and the privacy of algorithm parameters of a server are protected. The cloud server stores parameters, used for object detection, of various algorithms such as a human face detection algorithm. A client randomly divides the pictures into 256 binary images and sends the binary images to the server, and the server performs blind detection and returns an obtained detection result to the client. Random number mechanisms are introduced in both the server and the client, so that the privacy of images of customers and the privacy of the algorithm parameters of the server are protected. According to the method, random sub-graphs are applied to an object detection security protocol for the first time; any encryption algorithm is not introduced, so that the engineering application efficiency is greatly improved on the premise of ensuring data security of the server and the client; and the method is very easy to realize through software and can be widely applied and popularized to technologies of cloud computing, secure image convolution and the like.
Description
technical field [0001] The invention belongs to the fields of cryptography and computer vision, in particular to a hidden object detection method, in particular to a hidden object image detection method in a cloud environment based on a random subgraph representation. Background technique [0002] Face detection refers to determining the position and size of all faces (if present) in the input image. The input of the face detection system is an image that may contain a face, and the output is a parametric description of whether there is a face in the image and the number, position, and scale of the face. At present, there are many face detection algorithm models, such as ANN model, SVM model, Adaboost model, etc. The Viola&Jones face detection algorithm uses the Adaboost model, which is optimal in terms of speed, robustness and precision. Therefore, the face detection algorithm used in this paper is the Viola&Jones face detection algorithm. [0003] With the proliferation ...
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IPC IPC(8): G06T1/00
CPCG06T1/0021
Inventor 金鑫袁鹏李玉珍李晓东赵耿吴亚明马铭鑫田玉露陈迎亚
Owner BEIJING ELECTRONICS SCI & TECH INST