Edge Adaptive Image Steganography Based on LSB Matching Revisited

The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain.

Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions.

Existing System:

However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message.

We find that the existing PVD-based approaches cannot make full use of edge information for data hiding, and they are also poor at resisting some statistical analyses.

Proposed System:

We expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image.

For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters.

Modules:

  • Registration module

  • Finger print embedding module

  • Collision attack module

  • Authentication module

  • Verification module

  • LSB MODULE