Name
Title | ||
|---|---|---|
A novel adaptive steganography based on local complexity and human vision sensitivity |
Abstract | ||
|---|---|---|
This paper presents a novel adaptive steganographic scheme that is capable of both preventing visual degradation and providing a large embedding capacity. The embedding capacity of each pixel is dynamically determined by the local complexity of the cover image, allowing us to maintain good visual quality as well as embedding a large amount of secret messages. We classify pixels into three levels based on the variance of the local complexity of the cover image. When determining which level of local complexity a pixel should belong to, we take human vision sensitivity into consideration. This ensures that the visual artifacts appeared in the stego image are imperceptible, and the difference between the original and stego image is indistinguishable by the human visual system. The pixel classification assures that the embedding capacity offered by a cover image is bounded by the embedding capacity imposed on three levels that are distinguished by two boundary thresholds values. This allows us to derive a combination ratio of the maximal embedding capacity encountered with at each level. Consequently, our scheme is capable of determining two threshold values according to the desired demand of the embedding capacity requested by the user. Experimental results demonstrated that our adaptive steganographic algorithm produces insignificant visual distortion due to the hidden message. It provides high embedding capacity superior to that offered by a number of existing schemes. Our algorithm can resist the RS steganalysis attack, and it is statistically invisible for the attack of histogram comparison. The proposed scheme is simple, efficient and feasible for adaptive steganographic applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.jss.2010.01.050 | Journal of Systems and Software |
Keywords | Field | DocType |
steganography,embedding capacity,human vision sensitivity,local complexity analysis,visual artifact,stego image,human visual system,cover image,novel adaptive steganography,maximal embedding capacity,local complexity,good visual quality,large embedding capacity,high embedding capacity | Steganography,Histogram,Computer vision,Visual artifact,Embedding,Computer science,Human visual system model,Artificial intelligence,Pixel,Steganalysis,Bounded function | Journal |
Volume | Issue | ISSN |
83 | 7 | The Journal of Systems & Software |
Citations | PageRank | References |
13 | 0.63 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Der-Chyuan Lou | 1 | 468 | 37.88 |
| Nan-I Wu | 2 | 178 | 7.64 |
| Chung-Ming Wang | 3 | 331 | 21.16 |
| Zong-Han Lin | 4 | 13 | 0.63 |
| Chwei-Shyong Tsai | 5 | 654 | 38.63 |