# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 2 Related Work  
In this section, the existing and popular filtering detection techniques are briefly reviewed. It has been observed that there are many works [10; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] for detecting filtering in which most of works give emphasis on median filtering detection due to its non-linear nature. Only some methods in [10; 23] exist for detecting Gaussian filtering and for median, mean and Gaussian filtering in [23; 24; 31]. The existing techniques can be categorized as the autoregressive model, the CNN model, frequency domain, and Markov chain. [PERSON] et al. [16] introduced the autoregressive model for median filtering detection. Median filter residuals are considered and three median filter residuals arrays are generated after applying median filter of different size. The size of feature vector is small though a performance degrades on low resolution images. [PERSON] and [PERSON] [17] also generated numerous median filter residuals arrays after applying median filter multiple times. The eight quality matrices are calculated on residual arrays. The SVM classifier is applied to classify the images using quality matrices as a feature vector. The method fails on low resolution and compressed images. [PERSON] et al. [21] proposed a two dimensional autoregressive (2D-AR) model. The 2D-AR model was applied on median filtered residual (MFR), average filtered residual (AFR) and Gaussian filtered residual (GFR). Multiple residuals are still considered the accuracy decreases on small sized images. [PERSON] [27] proposed a technique that utilizes the autoregressive model, edge detectors and Hu moments. The hybrid feature set is not able to tackle the compression issue. In addition, [PERSON] [28] also applied the autoregressive model on bit plane sliced images. [PERSON] applied the autoregressive model on bit plane sliced median filtered residual arrays. The least and highest significant bits are considered and the performance is verified on resampled images. [PERSON] et al. [31] proposed a hybrid feature set using autoregressive model and local binary pattern (LBP) operator. The proposed model was able to classify median, mean and Gaussian filtered images. [PERSON] et al. [18] introduced the convolutional neural network (CNN) for median filtering detection. The new CNN layer is introduced to provide median filter residual. The performance of method is superseded by [PERSON] et al. [22] by proposing a CNN model using two multilayer perceptron (mlpconv) layers. The mlpconv layer is claimed more suitable for nonlinear data learning. [PERSON] and [PERSON] [23] introduced a constrained convolutional layer to highlight the image features. The method can detect median filtering, Gaussian blurring, resampling and JPEG compression. [PERSON] et al. [29] applied the high pass filter on the image and resultant image is given as input to CNN. [PERSON] et al. [35] proposed a CNN model by introducing an adaptive filtering layer (AFL). The proposed AFL is constructed to capture the frequency traces of median filtering in the DCT domain. The performance of CNN techniques is satisfactory on low resolution images although computation cost is very high and is required large dataset for training. [PERSON] and [PERSON] [19] proposed a method for Gaussian filtering detection. The Gaussian filter residual and frequency transform residual features are utilized for detection. The fast Fourier transform is used to convert from the spatial domain to the frequency domain. [PERSON] et al. [20] also considered the Fourier transform for the frequency domain. In [PERSON]'s method, it is discovered that most of the values are zero in a low-frequency region of the median filtered image in contrast to a non-filtered image. [PERSON] and [PERSON] [24] proposed a local quadruple pattern and extracted features in discrete cosine transform (DCT) to detect median, mean and Gaussian filtering. [PERSON] et al. [25] also proposed a method using the DCT domain.