Biblio

The most representative method of deep learning is data-driven learning. These methods are often data-dependent, and lack of data leads to poor learning. There is a GAN method that creates a likely image as a way to solve a problem that lacks data. The GAN determines that the discriminator is fake/real with respect to the image created so that the generator learns. However, overfitting problems when the discriminator becomes overly dependent on the learning data. In this paper, we explain overfitting problem when the discriminator decides to fake/real using xAI. Depending on the area of the described image, it is possible to limit the learning of the discriminator to avoid overfitting. By doing so, the generator can produce similar but more diverse images.

This paper presents the design and the verification of an efficient image protection method based on the QR code, which is a type of two-dimensional barcode widely used in various fields. For this purpose, we design a new image protection system consisting of a secure image generator and a secure image recognizer. One adds a new pre-processing block to the typical QR code generator and the other combines the existing QR code reader with a new post-processing block. The new architecture provides image de-identification. It is also flexible, allowing the use of text-based compression and encryption. We have implemented prototype applications for verifying the functions of the secure image generator and those of the secure image recognizer. As a result, it is shown that the proposed architecture can be used as a good solution for image privacy protection, especially in offline environments.

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. This paper introduces an intelligent middleware based on an unsupervised learning technique called Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a detector (D) network. The G creates fake data similar to the real samples and combines it with real data from the sensors to confuse the attacker. The D contains multi-layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. Results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting data from adversaries. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques.

Elliptic Curve Cryptography (ECC) is a promising public key cryptography, probably takes the place of RSA. Not only ECC uses less memory, key pair generation and signing are considerably faster, but also ECC's key size is less than that of RSA while it achieves the same level of security. However, the magic behind RSA and its friends can be easily explained, is also widely understood, the foundations of ECC are still a mystery to most of us. This paper's aims are to provide detailed mathematical foundations of ECC, especially, the subgroup and its generator (also called base point) formed by one elliptic curve are researched as highlights, because they are very important for practical ECC implementation. The related algorithms and their implementation details are demonstrated, which is useful for the computing devices with restricted resource, such as embedded systems, mobile devices and IoT devices.