# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 1 Introduction  
Today, most of the communication is being done in digital form, which opens a Pandora box of security attacks such as editing, copying, deleting and so on. Sometimes, the editing operation which does not require any expertise due to the wide availability of ubiquitous digital imaging devices and sophisticated editing software, may create an uproar as fake (edited) images and can be circulated through popular social media platforms to mislead the users as shown in Figure 1. Figure 0(a) shows that the anti-gun activist and student [PERSON] is tearing the copy of US constitution. Figure 1: Two pictures: (**a**) fake picture; (**b**) original picture. Whereas, the real picture is shown on the right side published in Teen Vogue magazine that is tearing the gun range target photo. The fake image was circulated on social media more than 70,000 times to get some political mileage in 2018. Therefore, the need of forensic toolboxes is required strongly which can assess blindly the authenticity of digital images without access to the source image, source device or the aid of an auxiliary watermark signal. In the recent years, image forgery detection techniques [1; 2; 3] based on handcrafted features and CNN features have been introduced. [PERSON] et al. [1] proposed a physics-based method that considers the 2-D lighting environments of objects. The incident of the light assists in detecting fake objects in the image. The method is verified on high-resolution challenging databases and has proved its superiority for some existing techniques, however there is still sufficient gap for improvement. [PERSON] et al. [2] exploited the discrete cosine transform (DCT) domain and singular value decomposition for image forgery detection. The method uses moments as features and support vector machine (SVM) as a classifier. The authors claim the good accuracy on multiple databases although performance may be degraded on cross-dataset and unknown image. Deep networks are found effective in many applications like face reorganization [4], object classification [5], facial expression [6] and so on. [PERSON] et al. [3] proposed a CNN based feature extractor for high-resolution images. Unlike other CNN techniques, the gradient check-pointing is applied for feature extraction on entire original image size. [PERSON] et al. 's technique extracts better features than other techniques in which the image is divided into overlapping patches to extract features. However, it has a high computational cost as full-size images are being used in feature extraction. In addition to the aforementioned handcrafted features and CNN features-based techniques, there has been discussed several image forgery detection techniques which are based on hardware or camera fingerprints, lighting environment, illumination, statistical information, deep networks and so on. In spite of this, some techniques [7; 8; 9] are also designed to counter image forensic and forgery detection techniques. [PERSON] et al. [7] designed the technique to counter the image forgery detection techniques. [PERSON] et al. [8] applied the adversarial Networks to remove the artifacts of median filtering. [PERSON] et al. [9] discovered that CNN based techniques are easy to conquer than machine learning based image forgery detection techniques while fake images are generated by an adversarial network. To make the fake images more realistic, the malicious intent people use multiple operations such as rotation, resampling, contrast enhancement so that the traces of modifications are hidden. Thus, the performance of image forgery detection methods is adversely affected by the filtering operations such as median filter, Gaussian filter, and mean filter. In particular, the median filtering (MF) is preferred among some forgery because it has the characteristics of nonlinear filtering based on order statistics. Therefore, a Markov chain based median filtering detector using a high significant bit-plane is proposed in this paper.