Klasifikasi Bit-Plane Noise untuk Penyisipan Pesan pada Teknik Steganography BPCS Menggunakan Fuzzy Inference Sistem Mamdani

Abstract

Bit-Plane Complexity Segmentation (BPCS) is a fairly new steganography technique. The most important process in BPCS is the calculation of complexity value of a bit-plane. The bit-plane complexity is calculated by looking at the amount of bit changes contained in a bit-plane. If a bit-plane has a high complexity, the bi-plane is categorized as a noise bit-plane that does not contain valuable information on the image. Classification of the bit-plane using the set cripst set (noise/not) is not fair, where a little difference of the value will significantly change the status of the bit-plane. The purpose of this study is to apply the principles of fuzzy sets to classify the bit-plane into three sets that are informative, partly informative, and the noise region. Classification of the bit-plane into a fuzzy set is expected to classify the bit-plane in a more objective approach and ultimately message capacity of the images can be improved by using the Mamdani fuzzy inference to take decisions which bit-plane will be replaced with a message based on the classification of bit-plane and the size of the message that will be inserted. This research is able to increase the capability of BPCS steganography techniques to insert a message in bit-pane with more precise so that the container image quality would be better. It can be seen that the PSNR value of original image and stego-image is only slightly different.