A Novel Technique of Securing Images using Chaos and EZW

II. CHAOS and EZW A. Chaos-based stitching algorithm: "Chaos" means "disordered state", it is a system called chaos system in mathematics and satisfies the following conditions [6] Should be sensitive to. It should satisfy the mixing performance. Periodic tracks should be dense. Chaotic mapping is a function that shows chaotic behavior; there are various types of chaotic maps (1-D mapping, 2-D mapping, discrete-time parameters, continuous time parameters, etc.).

In 2007 Younes & Jantan is proposing a simple and powerful image security method combining block-based image conversion and encryption technology. The original message is divided into blocks and relocated to the transformed image using a transformation algorithm, then the transformed image is encrypted using the Blowfish algorithm. Without a key, you can not perform encryption and decryption processes. The following figure shows the model of the proposed method. Keys provided according to different passwords have similar images. Sending image-based data using the appropriate user-defined key ensures confidentiality and security. Since the hash result is embedded in the image file, the resulting image is not generated. The following figure shows the proposed architecture.

In this article, let's explore some of the most common image enhancement techniques, including code examples and visualization of enhanced images. From now on, the data is called an image. All examples use Tensorflow or OpenCV written in Python. This is the technical index used in the article. Images collected from the Internet are different in size. Since most neural networks have a fully connected layer, the image input to the network must be fixed size (unless you use the spatial pyramid pool before passing it to the high density layer). So let's preprocess the image to the size required for our network before image enhancement happens. With fixed size images, you can gain the benefit of batch processing them.