# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 1 Introduction
The Markov chain is popular in many applications such as steganalysis, object classification, atmospheric science, and even in median filtering detection. The prominent median filter detector using Markov Chain was proposed by [PERSON] and [PERSON] [10] which uses difference arrays for Markov chain. Alike method is also proposed by [PERSON] et al. [11] for steganalysis and popularly known as SPAM. The method extracts features using Markov chain on thresholded difference array that results into more granular collection of features and ultimately leads to a higher accuracy. [PERSON] et al. [12] considered the absolute difference while applying thresholding to minimize the size of feature set so that the complexity of the method can be reduced. [PERSON] et al. [13] calculate the variance of difference arrays and use the probability of zero elements to decide whether an image is median filtered or not. [PERSON] et al. 's method is close to Markov model. [PERSON] and [PERSON] [14] extended the work of [PERSON] and [PERSON]'s method [10] by considering higher order differences. The incorporation of higher order differences collects even the minute details of images that help in effective collection of feature sets. The extraction of features is executed with the help of Markov chain on the first, second and third order thresholded difference arrays. Thus, [PERSON] and [PERSON]'s method could improve upon [PERSON] and [PERSON]'s method. However, it has been observed that the thresholded array and absolute difference can be loose some relevant information which may be useful in more accurate detection. In this paper, a high significant bit-plane based image forgery detection strategy is proposed to retain vital information. The main contributions of the proposed method can be summarized as follows:  
* The proposed scheme can reduce the bit depth to attain more relevant information in difference arrays. Higher significant bit-planes are considered for strong statistical analysis. * Various derivatives such as pixel difference, separate pixel difference, and the Laplacian operator are considered for a robust feature vector that can help in collecting additional information so that accuracy can be further enhanced. Further, the co-occurrence statistics is extracted from derivatives using the Markov chain. * Experimental results are compared with some of the popular methods such as GLF, GDCTF, PERB and SPAM to evaluate the performance of the proposed scheme. For the exhaustive analysis, the experimental results are shown for median filtered, mean filtered and Gaussian filtered images. * The experimental analysis shows that the proposed method has better performance than existing methods in most of the scenarios. There is a significant improvement of more than 2% in detection accuracy for the case of 3 \(\times\) 3 size filter on the low-resolution images and highly compressed images. The rest of the paper is organized as follows. In Section 2, existing techniques are discussed. The proposed technique is elaborated in Section 3. In Sections 4 and 5, experiment results and conclusion of the paper are discussed respectively.