Steganography is a method of hiding secret information within non-secret information. For the purpose of steganography, a lot of works based on convolutional neural network(CNN) were framed recent years and they showed the improvement of deep learning particularly in the field of hiding information. The major key factors that were kept in the account by those works include enhancing the capacity, invisibility, and security. In this research, a work based on steganography via generative adversarial networks was utilized to increase the invisibility and security, thus extracting that same secret image at the receiver side precisely. The focus of this research was to select the best suitable optimizer for the image based Steganography. Here, Stochastic Gradient Descent (SGD) and Adaptive Momentum (Adam) were compared and from the investigation, it was concluded that Adam optimizer performs better in handling the model to improve the hiding and revealing ability.
Verification of customer in web based banking system is a significant issue these days where exchanges are done utilizing uncertain Internet. The advanced communication medium is particularly experiencing a lot of threats. Picture identification and One Time Password (OTP) were commonly used to authenticate the customer over many banking systems. In most of the cases they were sent separately which is vulnerable in many cases. To solve this issue, this paper aims to give a method using both the image with hidden customer information and the OTP which is sent as SMS to user mobile. Personal Identification Number (PIN) provided by the bank at the time of registration is used to activate the process of image steganography and sending OTP to the user. The user has to know the image which was opted at the time of registration. The OTP has to be entered in a virtual keypad that has random keys to avoid key logging, used for decrypting the information hidden in the image. The image, the hidden information should match with the information in the database, thus providing the session for the customer.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.