Kensuke Naoe

Damageless image hashing using neural network

In this paper, we present a new key generation model for image hashing using neural network, which does not embed any data into the content but is able to extract meaningful data from target image. This model trains artificial neural network to assign predefined code and uses this trained artificial neural network weight and the coordinates of the selected feature sub blocks of target image as keys to extract the predefined code. In this model, the observed output signal from the trained neural network is used as image hash value which distinguishes the target image from other images. The proposed method contributes to secure image hashing for content identification without damaging or losing any detailed data of visual images. The proposed method realizes an application for image authentication, image similarity comparison, verification of image integrity and copyright protection of multimedia contents.

Damageless Information Hiding Technique Using Neural Network

In this paper, we propose a new information hiding technique without embedding any information into the target content by using neural network trained on frequency domain. Proposed method can detect a hidden bit codes from the content by processing the selected feature subblocks into the trained neural network. Hidden codes are retrieved from the neural network only with the proper extraction key provided. The extraction key, in proposed method, are the coordinates of the selected feature subblocks and the network weights generated by supervised learning of neural network. The supervised learning uses the coefficients of the selected feature subblocks as set of input values and the hidden bit patterns are used as teacher signal values of neural network. With our proposed method, we are able to introduce a information hiding scheme with no damage to the target content.

Micro Secure Socket Layer for Micro Server

In this paper, we propose Micro Secure Socket Layer (MSSL) for 8-bit flash micro controller that is about 1.3 Kbytes in code size. We have analyzed and compared various cryptographic protocols in TCP/IP stack for 5

A new log-spectral amplitude estimator using the weighted Euclidean distortion measure for speech enhancement

Micro Server to propose a simple secure layer based on simple handshake processing and encryption. Additionally, we analyzed MSSL performance, compared with the current security protocols and proved that MSSL can secure the connection between Micro Server and Internet. Security implementation for Micro Server, which has very limited size of memory with a small processer, is very difficult and challenging task. Conventional researches have focused mainly on creating small sensor system and Micro Server that has multi-function with low cost and small memory size, but without much security considerations. Crackers can easily access to the sensor nodes or Micro Server. Therefore, minimum security is needed in the ubiquitous sensor networks. The proposed MSSL is very small in code size so it can be implemented and is suitable for small sensors and Micro Server systems.

Information Hiding by Machine Learning: A Method of Key Generation for Information Extracting Using Neural Network

This paper considers a perceptually motivated estimator for single-channel speech enhancement based on statistics and Bayesian estimation in the frequency domain. In detail, we propose a new speech log-spectral amplitude estimator where the cost function is the weighted Euclidean distortion measure of the speech log-spectral amplitude. That cost function is motivated by auditory masking effects of the human hearing system. The statistical assumptions used to develop the proposed estimator are the complex Gaussian distribution and independence of speech, noise discrete Fourier transform coefficients. We evaluate this estimator with speech signals contaminated by various noise sources at different input signal-to-noise ratios and find that it achieves better performance than the well-known minimum mean square error log-spectral amplitude estimator in terms of both noise reduction and speech quality.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

In this paper, the authors propose information hiding by machine learning: a method of key generation for information extracting using neural network. The method consists of three layers for information hiding. First, the proposed method prepares feature extraction keys, which are saved by feature extraction attributes like feature coordinates and the region of frequency coefficients. Second, the proposed method prepares hidden patterns in advance to the embedding procedure as a watermark signal of the target contents. Finally, the proposed method generates information extraction keys by using machine learning to output presented hidden patterns. The proper hidden patterns are generated with the proper information extraction key and feature extraction key. In the experiments, the authors show that the proposed method is robust to high pass filtering and JPEG compression. The proposed method contributes to secure visual information hiding without damaging any detailed data of the target content.

Simplified IPSec Protocol Stack for Micro Server

The Service-Oriented Architecture SOA demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

Damageless Digital Watermarking by Machine Learning: A Method of Key Generation for Information Extraction Using Artificial Neural Networks

In this paper, we propose a simple IPSec protocol stack for Micro Server. We proposed an implementation of IPSec protocol stack which is constructed by Encapsulating Security Payload (ESP) protocol with Advanced Encryption Security (AES) encryption scheme, whereas authentication using MD5 algorithm is optional. Researchers have focused on creating a small system composed of sensors and a Micro Server where it has a small sized memory, multi-function, low cost, but without security consideration. The security problem in the Micro Server is a challenging task because of the very limited flash memory. Here, we have implemented the AES function as 2.704 Kbytes and the ESP protocol with this encryption function as 3.822Kbytes of code. Therefore, the proposed method has less than 4Kbytes in code size. Even including the authentication using MD5, the file size is less than 7Kbytes although this is still optional. In our proposed method, we have focused on implementing the encapsulation of the payload and ignored the key exchange procedure to simplify the secure communication compared to conventional IPSec protocol stack.

Key generation for static visual watermarking by machine learning

Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.

Micro Secure Socket Layer (MSSL) for micro server

Digital watermarking became a key technology for protecting copyrights. In this paper, we propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology.