Dynamic digital watermark technique based on neural network

A novel compact image encryption system is proposed based on Arnold transformation (AT). In general, AT is a pixel-scramble tool in image encryption, but its security is not high enough as an independent cryptosystem because of its periodicity. Hence, extra diffusion operations are needed to diffuse the pixel values to acquire the higher security. Here an encryption algorithm is proposed by AT. Firstly, the corresponding coordinates of each pixel in original image are calculated by AT. Secondly, the pixel values of the two corresponding coordinates are disturbed by another AT. These two processes can diffuse the pixel values of the original image. At last, the diffused image is pixel scrambled by AT with different parameters. This is the confusion process. In proposed scheme, the diffusing and confusing operations are both realized by AT. At the same time, the image is encrypted as a pixel distribution instead of a data stream. The key space, key sensitivity, the correlation, the cost time and the security of proposed cryptosystem are analyzed and the results confirm that the proposed image encryption algorithm demonstrates extraordinary performance.

In order to combine the copyright protection with the content authentication commendably in the digital watermark, this paper proposes an improved error correction coding -based multipurpose method where the authentication watermark and the robust watermark are embedded in the different coefficients of DCT transform domain respectively. In addition, to strengthen the security, the method still utilizes the error correction coding. Through a series of experiment, the invisibility of the method can be proved both from the PSNR value (PSNR=51.2 dB) and the histogram equalization. The similarity between the original watermark and extracted watermark is almost above 95%. The proposed method is effective in image authentication and it's robust against some image processing operations, such as low-pass filtering, Gaussian noise, JPEG lossy compression, cropping and histogram equalization.

The development of an image fusion system is described in this paper. The system consists of thermal and visible (color) cameras. Image alignment between two camera views is performed by using homography. The homography was estimated from four pairs of corresponding points on a planar object in the captured images that was manually selected by the user. Then, the user-defined region of interest (ROI) is used to adjust scale and position of the two images. Finally, the image fusion algorithm is applied to create the fused image. By thresholding the thermal image at a specified intensity level, the pixel values in the color image will be replaced with a predefined color if the corresponding pixels in the thermal image exceed the threshold value. This technique can provide thermal information of possible targets to be fused with original color image. This makes the system suitable for situation awareness. The experimental results show that the system can operate in real-time with satisfactory results.

This letter presents a method for digital image watermarking for copyright protection. This technique produces a watermarked image that closely retains the quality of the original host image while concurrently surviving various image processing operations such as lowpass/highpass filtering, lossy JPEG compression, and cropping. This image watermarking algorithm takes full advantage of permutation and 2-D barcode (PDF417). The actual watermark embedding in spatial domain is followed using permutated image for improving the resistance to image cropping. Much higher watermark robustness is obtainable via a simple forward error correction technique, which is the main feature of PDF417 codes. Additional features of this technique include the easy determination of the existence of the watermark and that the watermark verification procedure does not need the original host image. The experimental results demonstrate its effectiveness.

A chaotic image encryption algorithm based on random dynamic mixing

Purpose: The purpose of this study is to develop a new correction method for converting pixel values to Hounsfield units (HUs) using the histograms of pixel values for the cone beam computed tomography (CBCT) to calculate the dose distributions on the CBCT images. Methods: The simulation computed tomography (sim-CT) and CBCT images of an electron density phantom and 5 prostate cancer patients were acquired. The histograms of pixel values for each slice of the sim-CT and CBCT images of the electron density phantom and of the prostatic cancer patients were obtained with an in-house program. To perform correction of the pixel values for each slice of the CBCT images, the shapes of the histograms for each slice of the CBCT images were closed to the shapes of the histograms of the sim-CT images using linear scaling method with a minimum sum of squared differences for the regions of soft tissue and bone. To evaluate clinical significance, the dose distribution on the mCBCT images was compared with the original plan on the sim-CT images by using gamma analysis with a 3% / 2 mm criterion. Results: The linear scaling of pixel values in the CBCT images was successfully applied to the histograms. After the linear scaling, the histograms of mCBCT resembled the histograms of sim-CT. Thus, it is possible to judge pixel values for CBCT images in HUs. However, because the artifacts peculiar to CBCT remained, differences between both HUs were observed The pass rate of gamma analysis (3% / 2 mm) were more than 95%. Conclusion: The proposed novel method using histograms is a feasible method for converting pixel values in CBCT images to HUs; this may facilitate calculation of accurate doses using CBCT images.

In this paper, a new approach to identify and valid ate the F/A-18 aeroservoelastic model based on flight flutter tests is presented. The Neu ral Network, trained with five different flight flutter cases, is validated using eleven oth er flight flutter test data. The total of sixteen flight flutter tests cases were obtained fo r all three flight regimes (subsonic, transonic and supersonic) at Mach numbers between 0.85 and 1.30 and altitudes between 5,000 feet and 25,000 feet. The obtained r esults highlight the efficiency of the multi-layer perceptron Neural Network in model identification. The Neural Network optimization is required mixing hidden layer size r eduction and four-layered Neural Network performances. This article shows that a four-layered Neural Network with only 16 neurons is sufficient to create an accurate mode l. The fit coefficients are higher than 92%, either for the identification test data or the validation ones, and thus the Neural Network accuracy was demonstrated.

Steganography is the method of providing Computer security in which hiding the required information is done by inserting messages within other messages, which is a string of characters containing the useful information, in a carrier image. Using this technique, the required information from the secret image is embedded into individual rows as well as columns present in the pixels of carrier image. In this paper, a novel fuzzy logic based technique is proposed to hide the secret message in individual rows and in individual columns of pixels of the carrier image and to extract the hidden message in the same carrier image. The fuzzification process transforms the image in to various bitplanes. Pixel number and Correlation Value is computed in the original image for hiding the secret information in to the original image. The pixel number and Correlation value is also used as the key for retrieving the embedded image from the receiver side. Pixel merging is done in the sender side by assigning a steganographic value of white and black pixels in original image based on the fuzzy rules by comparing the pixels present in the original and secret images. The information which is hided can be retrieved by using the same fuzzy rules. Experimental results show that the proposed method can hide and retrieve the secret and important messages in an image more effectively and accurately.

This paper presented a hybrid neural network model to integrate information entropy theory and ant colony clustering for load forecasting. As short-term load forecasting is a complex problem with multifactor in power system, if all these factors are used as inputs of neural network, it will not only result in complicated network structure, but also long learning time and inaccurate prediction. First, information entropy theory is used to select relevant ones from all load influential factors, the results are used as inputs of neural network. It can reduce irrelevant load influential factors and the input variables of the input layer for neural network. Next, considering the features of power load and reduced influential factors, using ant colony clustering method, the practical historical load data within one year is divided into several groups. A separate module based on neural networks models each group. Then, the typical samples in each clustered group were selected as the training set for the separate improved Elman neural network which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. According to the procedures, the reduced input variables and the typical training samples for each neural network can be gotten. Thus the neural network forecasting model based on information entropy and ant colony clustering can be constructed which can effectively reduce the training time and improve convergent speed. During the forecasting process, pattern recognizing is employed to activate the corresponding module for hourly load forecasting. The presented model was tested using Hebei Province daily load data, and the satisfactory results were obtained.

Liquid crystal displays (LCDs) have appeared in applications ranging from medical equipment to automobiles, gas pumps, laptops, and handheld portable computers. These display components present a cascaded energy attenuator to the battery of the handheld device which is responsible for about half of the energy drain at maximum display intensity. As such, the display components become the main focus of every effort for maximization of embedded system's battery lifetime. This paper proposes an approach for pixel transformation of the displayed image to increase the potential energy saving of the backlight scaling method. The proposed approach takes advantage of human visual system (HVS) characteristics and tries to minimize distortion between the perceived brightness values of the individual pixels in the original image and those of the backlight-scaled image. This is in contrast to previous backlight scaling approaches which simply match the luminance values of the individual pixels in the original and backlight-scaled images. Furthermore, this paper proposes a temporally-aware backlight scaling technique for video streams. The goal is to maximize energy saving in the display system by means of dynamic backlight dimming subject to a video distortion tolerance. The video distortion comprises of: 1) an intra-frame (spatial) distortion component due to frame-sensitive backlight scaling and transmittance function tuning and 2) an inter-frame (temporal) distortion component due to large-step backlight dimming across frames modulated by the psychophysical characteristics of the human visual system. The proposed backlight scaling technique is capable of efficiently computing the flickering effect online and subsequently using a measure of the temporal distortion to appropriately adjust the slack on the intra-frame spatial distortion, thereby, achieving a good balance between the two sources of distortion while maximizing the backlight dimming-driven energy saving in the display system and meeting an overall video quality figure of merit. The proposed dynamic backlight scaling approach is amenable to highly efficient hardware realization and has been implemented on the Apollo Testbed II. Actual current measurements demonstrate the effectiveness of proposed technique compared to the previous backlight dimming techniques, which have ignored the temporal distortion effect

Registration of multi-modal remote sensing images is an essential and challenging task in different remote sensing applications such as image fusion and multi-temporal change detection. Mutual Information (MI) has shown to be successful similarity measure for multi-modal image registration applications, however it has some drawbacks. 1. MI surface is highly non-convex with many local maxima. 2. Spatial information is completely lost in the calculation of the joint intensity probability distribution. In this paper, we present an improved MI similarity measure based on a new concept in integrating other image features as well as spatial information in the estimation of the joint intensity histogram which is used as an estimate of the joint probability distribution. The proposed method is based on the idea that each pixel in the reference image is assigned a weight, then each bin in the joint histogram is calculated as the summations of the weights of the pixels corresponding to that bin. The weight given to each pixel in the reference image is an exponential function of the corresponding pixel values in a distance image and a normalized gradient image such that higher weights are given to points close to one or more selected key points as well as points with high normalized gradient values. The proposed method is in essence a kind of calculating similarity measure using irregular sampling where sampling frequency is higher in areas close to key-points or areas with higher gradients. We have compared the proposed method with the conventional MI and Normalized MI methods for registration of pairs of multi-temporal multi-modal remote sensing images. We observed that the proposed method produces considerably better registration function containing fewer erroneous maxima and leading to higher success rate.

The purpose of this paper is to apply a neural network to classify aerial photographs. An adaptive backpropagation algorithm is introduced to build a four-layer neural network as the practical model to classify aerial photographs based on pixel-pixel. The practical example is the classification of lake, forest and land, comparing a neural network with the maximum likelihood classification through the analysis of the process of classification and the results. The four-layer neural network introduced in this paper succeeded in building the complex model as an aerial photograph and it solved the problem of the great storage of data. The adaptive back-propagation algorithm adopted in this paper speeded up the learning rate, the general error decreased to a smaller degree, and the generalization ability of the neural network was also improved. The results demonstrated that the neural network is suitable to be used in the classification of remotely sensed data and is superior to the maximum likelihood classifier in the accuracy of the classification and the overall effect.

A method of output prediction about crosstalk coupling of multi-cable bundle on aircraft is presented without solving the coupling matrix. BP neural network is used to simulate the coupling relation between input and output. The network of input and output are trained with LMBP algorithm. MOM is applied to calculate the output value of multi-cable bundle when inputs in different frequency point are given through establishing 3D model of transmission lines. It's shown that the predicted values of neural network in different frequency points coincide with values of MOM simulative outputs well. Otherwise a concept of relative coupling error is put forward to estimating the accuracy of prediction algorithm. The predicted values are much closer to the simulative ones after the inputs of neural network have been optimized properly. This method can be used to predict the output of nonlinear multi-cables coupling in complex system structure.

To realize the automatic removal of zinc slag in a hot-dip galvanizing pot, the recognition method of zinc slag based on features was studied. Three recognition algorithms of zinc slag based on image pixel value, SSIM (Structural Similarity Index), and image features are designed. MATLAB was used for experimental simulation, and the accuracy, precision, recall, F1- score, and execution efficiency of the three methods were compared. The results show that the zinc slag recognition method based on pixel value is more comprehensive, and the zinc slag recognition method based on statistical features has the highest accuracy, while the zinc slag recognition method based on SSIM has the best comprehensive effect in recognition.

Deals with the approximation of continuous functions by feedforward neural networks. After presenting one of the main results of Ito, the paper tries to get a universal approximator implementable as a (1+1/2) layer neural network using Heaviside functions as univariate functions. The paper presents an explicit formula for function approximation implementable as a three-layer feedforward neural network instead of a four-layer neural network. These three-layer feedforward neural networks have the same number of neurons in the hidden layer as the equivalent four-layer neural networks have in the second hidden layer.