Steganography and classification of image steganography techniques

Abstract

Information is wealth of any organization and in present era in which information transferred through digital media and internet, it became a top priority for any organizations to protect this wealth. Whatever technique we adopt for the security purpose, the degree and level of security always remains top concern. Steganography is one such technique in which presence of secret message cannot be detected and we can use it as a tool for security purpose to transmit the cofidential information in a secure way. It is an ongoing research area having vast number of applications in distinct fields such as defense and intelligence, medical, on-line banking, on-line transaction, to stop music piracy and other financial and commercial purposes. There are various steganography approaches exist and they differs depending upon message to be embedded, use of file type as carrier or compression method used etc. The focus of this paper is to classify distinct image steganography techniques besides giving overview, importance and challenges of steganography techniques. Other related security techniques are also been discussed in brief in this paper. The classification of steganography techniques may provide not only understanding and guidelines to researchers in this field but also provide directions for future work in this field.

Secure Electronic Voting Using a Hybrid Cryptosystem and Steganography

“…The altered new object is called stego-object. Image, audio, video and so on can be the cover objects [28,29,30]. For steganography algorithms, three concepts are required that contend with each other [31]:…”

An Overview and Computer Forensic Challenges in Image Steganography

“…Additionally, the easiest and simplest way to detect or suspect the existence of steganography is by using the natural eye [12]. Experts in steganalysis observe the presence of steganography when each bit of pixels is altered [13].…”

JPEG Image Steganography based on Coefficients Selection and Partition

“…JPEG compression has two levels: first DCT quantization, which forms the part of the lossy level and the second one, is the Huffman coding that compresses data lossless. JPEG image embedding stores secret data between these two phases [4]. DCT transformed cosine values cannot be calculated back exactly and repeated calculation using limited precision number produces a rounding error hence, it is called lossy compression.…”