Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images

A sliding window-based analysis is a prevailing mechanism for tampering localization in passive image authentication. It uses existing forensic detectors, originally designed for a full-frame analysis, to obtain the detection scores for individual image regions. One of the main problems with a window-based analysis is its impractically low localization resolution stemming from the need to use relatively large analysis windows. While decreasing the window size can improve the localization resolution, the classification results tend to become unreliable due to insufficient statistics about the relevant forensic features. In this paper, we investigate a multi-scale analysis approach that fuses multiple candidate tampering maps, resulting from the analysis with different windows, to obtain a single, more reliable tampering map with better localization resolution. We propose three different techniques for multi-scale fusion, and verify their feasibility against various reference strategies. We consider a popular tampering scenario with mode-based first digit features to distinguish between singly and doubly compressed regions. Our results clearly indicate that the proposed fusion strategies can successfully combine the benefits of small-scale and large-scale analyses and improve the tampering localization performance.

[1]  Alessandro Piva,et al.  Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[2]  Eduardo F. Nakamura,et al.  Information fusion for wireless sensor networks: Methods, models, and classifications , 2007, CSUR.

[3]  Martial Hebert,et al.  Discriminative random fields: a discriminative framework for contextual interaction in classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Chi-Keung Tang,et al.  Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis , 2009, Pattern Recognit..

[5]  Christian Riess,et al.  Ieee Transactions on Information Forensics and Security an Evaluation of Popular Copy-move Forgery Detection Approaches , 2022 .

[6]  A. De Rosa,et al.  Unsupervised fusion for forgery localization exploiting background information , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[7]  Yuan-Kai Wang,et al.  Single Image Defogging by Multiscale Depth Fusion , 2014, IEEE Transactions on Image Processing.

[8]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[9]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[10]  Stefan Harmeling,et al.  Improving Denoising Algorithms via a Multi-scale Meta-procedure , 2011, DAGM-Symposium.

[11]  Min Wu,et al.  Information Forensics: An Overview of the First Decade , 2013, IEEE Access.

[12]  Edward H. Adelson,et al.  PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .

[13]  Davide Cozzolino,et al.  Guided filtering for PRNU-based localization of small-size image forgeries , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Pawel Korus,et al.  Efficient Method for Content Reconstruction With Self-Embedding , 2013, IEEE Transactions on Image Processing.

[15]  Jing Dong,et al.  Exploring DCT Coefficient Quantization Effects for Local Tampering Detection , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Michael Elad,et al.  Image denoising through multi-scale learnt dictionaries , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[17]  Mauro Barni,et al.  A Framework for Decision Fusion in Image Forensics Based on Dempster–Shafer Theory of Evidence , 2013, IEEE Transactions on Information Forensics and Security.

[18]  Heinz H. Bauschke,et al.  Recompression of JPEG images by requantization , 2003, IEEE Trans. Image Process..

[19]  William T. Freeman,et al.  A computational approach for obstruction-free photography , 2015, ACM Trans. Graph..

[20]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[21]  Domènec Puig,et al.  Supervised texture classification by integration of multiple texture methods and evaluation windows , 2007, Image Vis. Comput..

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

[23]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Pawel Korus,et al.  Towards Practical Self-Embedding for JPEG-Compressed Digital Images , 2015, IEEE Transactions on Multimedia.

[25]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[26]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[28]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Mo Chen,et al.  Determining Image Origin and Integrity Using Sensor Noise , 2008, IEEE Transactions on Information Forensics and Security.

[30]  Christel Chamaret,et al.  Spatio-temporal combination of saliency maps and eye-tracking assessment of different strategies , 2010, 2010 IEEE International Conference on Image Processing.

[31]  Xiaochun Cao,et al.  Saliency map fusion based on rank-one constraint , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[32]  Chiou-Ting Hsu,et al.  What has been tampered? From a sparse manipulation perspective , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[33]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[34]  Ali Borji,et al.  Boosting bottom-up and top-down visual features for saliency estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[36]  Mauro Barni,et al.  Dealing with uncertainty in image forensics: A fuzzy approach , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[37]  Bin Li,et al.  Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis , 2015, IEEE Transactions on Information Forensics and Security.

[38]  Martin Kraus,et al.  Depth‐of‐Field Rendering by Pyramidal Image Processing , 2007, Comput. Graph. Forum.

[39]  Mohammad Ali Akhaee,et al.  A Source-Channel Coding Approach to Digital Image Protection and Self-Recovery , 2015, IEEE Transactions on Image Processing.

[40]  Moshe Porat,et al.  Toward optimal real-time transcoding using requantization in the DCT domain , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[41]  Raanan Fattal,et al.  Image and video upscaling from local self-examples , 2011, TOGS.

[42]  Luisa Verdoliva,et al.  A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection , 2014, IEEE Transactions on Information Forensics and Security.

[43]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[44]  Davide Cozzolino,et al.  Multiple Classifier Systems for Image Forgery Detection , 2013, ICIAP.

[45]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[46]  Roberto Caldelli,et al.  Splicing forgeries localization through the use of first digit features , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[47]  Bin Li,et al.  Detecting doubly compressed JPEG images by using Mode Based First Digit Features , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[48]  Ananthram Swami,et al.  Trust and independence aware decision fusion in distributed networks , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[49]  Paolo Bestagini,et al.  Multi-Clue Image Tampering Localization , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[50]  Alain Trémeau,et al.  A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes , 2013, 2013 IEEE International Conference on Image Processing.