NDLI: Robust median filtering forensics based on the autoregressive model of median filtered residual

Content Provider	IEEE Xplore Digital Library
Author	Xiangui Kang Stamm, M.C. Anjie Peng Liu, K.J.R.
Copyright Year	2012
Description	Author affiliation: School of Information Science & Technology, Sun Yat-Sen University, Guangzhou, GD 510006 China (Stamm, M.C.; Liu, K.J.R.) \|\| Dept. of Electrical and Computer Engineering, University of Maryland, College Park, 20742, USA (Xiangui Kang; Anjie Peng)
Abstract	One important aspect of multimedia forensics is exposing an image's processing history. Median filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of median filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust median filtering detection scheme based on the autoregressive model of median filtered residual. Median filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed median filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between median filtering and other manipulations, such as Gaussian filtering, average filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
Starting Page	1
Ending Page	9
File Size	507077
Page Count	9
File Format	PDF
ISBN	9781467348638
e-ISBN	9780615700502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	APSIPA
Subject Keyword	Image coding Filtering Forensics Image edge detection Transform coding Unsolicited electronic mail Robustness
Content Type	Text
Resource Type	Article

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