1.  Forensics of image re-sampling (such as image resizing) is an important issue,which can be used for tampering detection, steganography, etc.  Most of the algorithms employed the periodical artifacts, which are caused by regular sampling, to detect image re- sampling.  Kirchner et al. [1] proposed two anti-forensic methods by removing the periodic artifacts using irregular sampling, and defeated all periodicity-based methods.  Attack based on geometric distortion with edge modulation (attack 1)  The dual-path approach (attack 2)  Our work  Our work aims at distinguishing the forged image from the original image. It can be considered as patches of periodicity- based methods.  We find the anti-forensic scheme leaves behind new clues. The last step of the anti-forensic scheme is interpolation, which changes the linear relationships among neighboring pixels..  To capture such relationships, the partial autocorrelation coefficients of neighboring pixels are taken as the feature. [1] M. Kirchner and R. Böhme, “Hiding traces of re-sampling in digital images”, IEEE Trans. Inf. Forensics Security, vol. 3, no.4, pp. 582–592, Dec. 2008. Countering Anti-Forensics of Image Re-sampling Anjie Peng; Hui Zeng; Xiaodan Lin; Xiangui Kang School of Information Sci. & Tech., Sun Yat-sen Univ., China. Contact: isskxg@mail.sysu.edu.cn. Abstract Image re-sampling leaves behind periodical artifacts which are used as fingerprints for the forensics. A knowledgeable anti-forensic method erases such artifacts by irregular sampling. We observe that the irregular sampling followed by interpolation causes changes in local linear correlations, and propose a novel method to detect the anti-forensic method of re-sampling via partial autocorrelation coefficients. Experimental results on a large set of images show that the proposed method could effectively detect the anti-forensics of re-sampling with a low dimensional feature set. Introduction Experimental Results The Proposed Algorithm  Baseline test: classifying anti-forensics images from original images. Both two kinds are un-compressed.  Generalization ability test Tips: The classifier is trained on UCID database, while tested on BOSSbase. The test set has 10000 original images and 10000 anti-forensic forged images.  The goal of the proposed method aims at distinguishing the anti-forensic forged image from the original image.  Interpolation used in the anti-forensic scheme changes the relations among neighboring pixels, and thus leaves behind clues for detecting.  The partial autocorrelation is used to reveal the correlations among neighboring pixels. Given a sequence z, unlike the commonly used autocorrelation  k, the partial autocorrelation  kk is the autocorrelation between z t and z t+k with the linear dependency of z t+1 through to z t+k-1 removed. The estimation of  kk is as (3).  In this paper, we first analyze the processing chains of anti-forensics for image re-sampling, and then propose a novel feature set with low dimension to detect the anti- forensics of re- sampling.  Experimental results of baseline test and generalization ability test show that the proposed method could effectively detect the anti-forensics. Conclusion Tips:  SVM is used as a machine learning tool. All classifiers are trained and tested on the UCID database. Half images are used for training,other half are for testing.  Each test set has 669 original images and 669 anti-forensic forged images which are uniformly used scaling factors in [0.6, 2] with interval 0.1.  Pe means the minimal average decision error.  Robustness against JPEG compression Tips: The classifier is trained and tested on JPEG compressed UCID database. The anti-forensic. Forged image is generated by the following set (attack 1,  =0.4, kernel=‘bicubic’).  The partial autocorrelation feature (PAF) is extracted from both horizontal and vertical directions. Coefficients of two directions are averaged to get the PAF feature. Coefficients are extracted from image I and its 2nd-laplacian difference domain (denoted by D). The dimension of PAF is 12. PAF=[PAF I,PAF D ] Table 1. Pe （ % ） of the proposed scheme for 18 kinds of forged image. The second column shows the parameters used in attack,  : attack strength, kernel: interpolation algorithm.