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Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images | IEEE Journals & Magazine | IEEE Xplore

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

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 desi...View more

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.
Published in: IEEE Transactions on Image Processing ( Volume: 25, Issue: 3, March 2016)
Page(s): 1312 - 1326
Date of Publication: 18 January 2016

ISSN Information:

PubMed ID: 26800540
Publisher: IEEE

Funding Agency:


References

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