Using Support Vector Machine to Detect Data Hiding in Color Images

Abstract

Background:

A steganography investigation model that uses improved dim scale List of capabilities, for identifying information inside uncompressed RGB pictures.

Materials and Methods:

in this paper, a bunch of 2000 RGB pictures was made utilizing normal tones, in BMP design with a size of 512 x 512 pixels. Clean pictures are Inserted secret picture information, utilizing two Payload plans, 2 pieces for every channel (BPC) and 4 pieces for each channel. The arrangement of chosen highlights comprises of 24 elements for every variety channel, and 72 highlights for each variety channel The picture, incorporates dim-level co-event grid highlights, entropy Elements, and factual proportions of variety. The list of capabilities components is determined as Individual channels, joined into picture vector highlights. The cycle depends on steganography examination On AI, utilizing Paired vector machine (SVM) support Execute the exercise manual in MATLAB.

 Results:

The outcomes showed that the new method had overcome the benchmark with more than 80% identification accuracy

Conclusion :

a new Steganalysis model was introduced by using SVM. The model can recognize the presence of stowed-away information inside RGB variety pictures, removing the highlights from datasets of perfect and messy pictures and ordering utilizing the support Vector Machine calculation. the results were compared with the DNA algorithm.