Median filtering forensics in digital images based on frequency-domain features

Tampering detection has been increasingly attracting attention in the field of digital forensics. As a popular nonlinear smoothing filter, median filtering is often used as a post-processing operation after image forgeries such as copy-paste forgery (including copy-move and image splicing), which is of particular interest to researchers. To implement the blind detection of median filtering, this paper proposes a novel approach based on a frequency-domain feature coined the annular accumulated points (AAP). Experimental results obtained on widely used databases, which consists of various real-world photos, show that the proposed method achieves outstanding performance in distinguishing median-filtered images from original images or images that have undergone other types of manipulations, especially in the scenarios of low resolution and JPEG compression with a low quality factor. Moreover, our approach remains reliable even when the feature dimension decreases to 5, which is significant to save the computing time required for classification, demonstrating its great advantage to be applied in real-time processing of big multimedia data.

[1]  K. J. Ray Liu,et al.  Anti-forensics of digital image compression , 2011, IEEE Transactions on Information Forensics and Security.

[2]  Min Wu,et al.  Tampering identification using Empirical Frequency Response , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Rainer Böhme,et al.  The 'Dresden Image Database' for benchmarking digital image forensics , 2010, SAC '10.

[4]  Jiwu Huang,et al.  Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach , 2013, IEEE Transactions on Image Processing.

[5]  Yan Liu,et al.  How important is location information in saliency detection of natural images , 2015, Multimedia Tools and Applications.

[6]  Hai-Dong Yuan,et al.  Blind Forensics of Median Filtering in Digital Images , 2011, IEEE Transactions on Information Forensics and Security.

[7]  Yue Gao,et al.  Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval , 2016, IEEE Transactions on Image Processing.

[8]  Georg Heygster.  Rank filters in digital image processing , 1982, Comput. Graph. Image Process..

[9]  Sabu Emmanuel,et al.  Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[10]  Paul F. Velleman,et al.  Definition and Comparison of Robust Nonlinear Data Smoothing Algorithms , 1980 .

[11]  Alan C. Bovik,et al.  Streaking in median filtered images , 1987, IEEE Trans. Acoust. Speech Signal Process..

[12]  Nicu Sebe,et al.  Multi-task linear discriminant analysis for multi-view action recognition , 2013, 2013 IEEE International Conference on Image Processing.

[13]  Jessica J. Fridrich,et al.  On detection of median filtering in digital images , 2010, Electronic Imaging.

[14]  Gerald Schaefer,et al.  UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.

[15]  Weizhi Nie,et al.  3D object retrieval based on sparse coding in weak supervision , 2016, J. Vis. Commun. Image Represent..

[16]  Nicu Sebe,et al.  Event Oriented Dictionary Learning for Complex Event Detection , 2015, IEEE Transactions on Image Processing.

[17]  Tat-Seng Chua,et al.  Learning from Collective Intelligence , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[18]  Thomas S. Huang,et al.  Two-dimensional digital signal processing II: Transforms and median filters , 1981 .

[19]  Bin Wang,et al.  Reconstruction and analysis of a genome-scale metabolic model for Eriocheir sinensis eyestalks. , 2016, Molecular bioSystems.

[20]  Yue Gao,et al.  Beyond Text QA: Multimedia Answer Generation by Harvesting Web Information , 2013, IEEE Transactions on Multimedia.

[21]  Yuting Su,et al.  Multiple/Single-View Human Action Recognition via Part-Induced Multitask Structural Learning , 2015, IEEE Transactions on Cybernetics.

[22]  Rainer Böhme,et al.  Hiding Traces of Resampling in Digital Images , 2008, IEEE Transactions on Information Forensics and Security.

[23]  K. J. Ray Liu,et al.  Robust Median Filtering Forensics Using an Autoregressive Model , 2013, IEEE Transactions on Information Forensics and Security.

[24]  Z. Jane Wang,et al.  Median Filtering Forensics Based on Convolutional Neural Networks , 2015, IEEE Signal Processing Letters.

[25]  Weizhi Nie,et al.  Clique-graph matching by preserving global & local structure , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Yuting Su,et al.  Smooth filtering identification based on convolutional neural networks , 2019, Multimedia Tools and Applications.

[27]  Subramanian Ramanathan,et al.  A Multi-Task Learning Framework for Head Pose Estimation under Target Motion , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  B I Justusson,et al.  Median Filtering: Statistical Properties , 1981 .

[29]  Yao Zhao,et al.  Robust median filtering detection based on local difference descriptor , 2017, Signal Process. Image Commun..

[30]  Yun Q. Shi,et al.  Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns , 2014, IEEE Signal Processing Letters.

[31]  Mohan S. Kankanhalli,et al.  Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.