Employing Neural Style Transfer for Generating Deep Dream Images

In recent years, deep dream and neural style transfer emerged as hot topics in deep learning. Hence, mixing those two techniques support the art and enhance the images that simulate hallucinations among psychiatric patients and drug addicts. In this study, our model combines deep dream and neural style transfer (NST) to produce a new image that combines the two technologies. VGG-19 and Inception v3 pre-trained networks are used for NST and deep dream, respectively. Gram matrix is a vital process for style transfer. The loss is minimized in style transfer while maximized in a deep dream using gradient descent for the first case and gradient ascent for the second. We found that different image produces different loss values depending on the degree of clarity of that images. Distorted images have higher loss values in NST and lower loss values with deep dreams. The opposite happened for the clear images that did not contain mixed lines, circles, colors, or other shapes

Image Encryption Based on Multi-Level Keys on RC5 Algorithm

In recent years, the need to develop encryption algorithms has led to an increase in the working and efficiency of algorithms to protect the transmission and reception of information from any security breach. The RC5 type encryption algorithm is the most common and closest to perfection and symmetry algorithms, knowing that it faces many problems in which data collection was limited because it occurs only twice by working on its also, the algorithm is used for only 1 function (XOR) through the encryption process. A research report on digital image development by developing the RC5 algorithm makes that algorithm more secure by adding a new security level (using two keys) and thus increasing the key space. The encryption and decryption process can be done by substituting the XOR operation applied to Sixteen rounds of the algorithm with the new operation (#) based on the use of 2 keys, each of it consisting of 4 states (0,1,2,3) instead of using the traditional key that uses two states (0,1). This development of the RC5 algorithm increases the security and robustness of the hacking methods

Cyber Security for Medical Image Encryption using Circular Blockchain Technology Based on Modify DES Algorithm

Recently, with the requirement for protecting the privacy of images transferred over the internet and media networks. The need to protect these images from hacking by unauthorized persons and from the manipulation of these images has become very important in this research, Block chain technology was used for its importance in cybersecurity. To maintain the confidentiality of the patient's medical data, as a result, to solve this problem requires increasing the strength of the key used in the encryption process, which is responsible for ensuring the security of the image .In this paper, it is proposed to use block chain technology with the Data Encryption Standard (DES) algorithm for the purpose of increasing the degree of security of the transmitted images by enhancing the key during the process of encrypting the transmitted images as well as increasing the degree of authentication between the sender and receiver. Experimental outcomes manifested that the security of encryption image that gained via the suggested algorithm is higher, performing the goal of protecting the information of medical image, as presented by the results obtained in Entropy, MSE, and PSNR

The Effect of Changing Targeted Layers of the Deep Dream Technique Using VGG-16 Model

The deep dream is one of the most recent techniques in deep learning. It is used in many applications, such as decorating and modifying images with motifs and simulating the patients' hallucinations. This study presents a deep dream model that generates deep dream images using a convolutional neural network (CNN). Firstly, we survey the layers of each block in the network, then choose the required layers, and extract their features to maximize it. This process repeats several iterations as needed, computes the total loss, and extracts the final deep dream images. We apply this operation on different layers two times; the former is on the low-level layers, and the latter is on the high-level layers. The results of applying this operation are different, where the resulting image from applying deep dream on the high-level layers are clearer than those resulting from low-level layers. Also, the loss of the images of low-level layers ranges between 31.1435 and 31.1435, while the loss of the images of upper layers ranges between 20.0704 and 32.1625

A Hybrid Artistic Model Using Deepy-Dream Model and Multiple Convolutional Neural Networks Architectures

The significant increase in drug abuse cases prompts developers to investigate techniques that mimic the hallucinations imagined by addicts and abusers, in addition to the increasing demand for the use of decorative images resulting from the use of computer technologies. This research uses Deep Dream and Neural Style Transfer technologies to solve this problem. Despite the significance researches on Deep Dream technology, there are several limitations in existing studies, including image quality and evaluation metrics. We have successfully addressed these issues by improving image quality and diversifying the types of generated images. This enhancement allows for more effective use of Deep Dream in simulating hallucinated images. Moreover, the high-quality generated images can be saved for dataset enlargement, like the augmentation process. Our proposed deepy-dream model combines features from five convolutional neural network architectures: VGG16, VGG19, Inception v3, Inception-ResNet-v2, and Xception. Additionally, we generate Deep Dream images by implementing each architecture as a separate Deep Dream model. We have employed autoencoder Deep Dream model as another method. To evaluate the performance of our models, we utilize normalized cross-correlation and structural similarity indexes as metrics. The values obtained for those two quality measures for our proposed deepy-dream model are 0.1863 and 0.0856, respectively, indicating effective performance. When considering the content image, the metrics yield values of 0.8119 and 0.3097, respectively. As for the style image, the corresponding quality measure values are 0.0007 and 0.0073, respectively

Photodegradation of Carbol Fuchsin Dye Using an Fe2−xCuxZr2−xWxO7 Photocatalyst under Visible-Light Irradiation

Fe2−xCuxZr2−xWxO7 (x: 0, 0.05, 0.015) nanoparticles were synthesized following the Pechini method and characterized via X-ray diffraction (XRD), transmission electron microscopy (TEM), and diffuse reflectance spectroscopy (DRS) measurements to be used as photocatalysts in colored water remediation. All of the prepared materials were crystallized in a cubic fluorite phase as the major phase. The band gap was reduced upon doping with W6+ and Cu2+ from 1.96 eV to 1.47 eV for Fe1.85Cu0.15Zr1.85W0.15O7. Carbol fuchsin (CF) dye was used to determine the photocatalytic degradation efficiency of the prepared catalysts. Degradation efficiency was directly proportional to the dopant’s concentration. Complete removal of 20 mg/L CF was achieved under optimal conditions (pH 9, and catalyst loading of 1.5 g/L) using Fe1.85Cu0.15Zr1.85W0.15O7. The degradation rate followed pseudo-first-order kinetics. The reusability for photocatalysts was tested five times, decreasing its efficiency by 4% after the fifth cycle, which indicates that the prepared Fe1.85Cu0.15Zr1.85W0.15O7 photocatalyst is a promising novel photocatalyst due to its superior efficiency in dye photodegradation