Using Bias Optimiazation for Reversible Data Hiding using Image Interpolation

Abstract

In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method and the Neighbour Mean Interpolation (NMI 1.INTRODUCTIONSecret information exchange is a vital requirement of persons and institutions in society. Channelling secret information over public networks has proven to be very insecure. There is a great need for protective methods for sending secret information.Cryptography is traditionally the first method implemented for protection. Cryptography however, has a few drawbacks. Encrypted data can be easily identified during transmission hence attracting unwanted attention to the packaged information. With increased computing power the possibility of cracking the cryptographic technique increases. Information hiding is an alternative strategy that can be used to protect sensitive secret information. While cryptography protects the content of messages data hiding conceals the existence of secret information.In general, information hiding (also called data hiding or data embedding) includes digital watermarking and steganography [1].Data hiding technology prevents information from being detected, stolen or damaged by unauthorized users during transmission. The word stegano-graphy is a Greek word meaning "covered writing" the art of hiding secret information in ways that prevent detection [2]. The traditional method of encryption [3] can still be applied to the message and then a stegonographic approach used to ensure undetected delivery.Information can be hidden in many ways. Hiding information may involve straight message insertion whereby every bit of information in the cover is encoded or it may selectively embed messages in noisy areas that draw less attention. Messages may also be dispersed in a random fashion throughout the cover data.

Discrete Cosine Transformer Based Visual Secret Sharing With Chaos Visual Cryptography

“…Quantization Index Modulation (QIM) for Biometric Encryption (BE) in [9] introduced mechanisms to reduce the false acceptance and rejection rate. Neighbor Mean Interpolation (NMI) technique [10] was introduced to improve the embedding capacity resulting in low PSNR values. However, the mean square error rate was unaddressed in both QIM and NMI which our work solved through chaos based encryption properties.…”