Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617122
Title: Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images
Authors: Paweł Korus;Jiwu Huang
subject: Markov random fields|energy minimization|first-digit-features|result fusion|tampering localization|multi-scale analysis|digital image forensics
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/148026
http://localhost/handle/Hannan/617122
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
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Title: Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images
Authors: Paweł Korus;Jiwu Huang
subject: Markov random fields|energy minimization|first-digit-features|result fusion|tampering localization|multi-scale analysis|digital image forensics
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/148026
http://localhost/handle/Hannan/617122
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7384734.pdf3.21 MBAdobe PDFThumbnail
Preview File
Title: Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images
Authors: Paweł Korus;Jiwu Huang
subject: Markov random fields|energy minimization|first-digit-features|result fusion|tampering localization|multi-scale analysis|digital image forensics
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/148026
http://localhost/handle/Hannan/617122
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7384734.pdf3.21 MBAdobe PDFThumbnail
Preview File