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

Paweł Korus and Jiwu Huang

IEEE Transactions on Image Processing, Vol. 25, Issue 3, 2016

2016-tip-multiscale-jpeg.pdf (2 MB)

10.1109/TIP.2016.2518870

https://github.com/pkorus/jpeg-mbfdf-forensics

Abstract

Sliding window-based analysis is a prevailing mechanism for tampering localization in passive image authentication. It uses existing forensic detectors, originally designed for full-frame analysis, to obtain the detection scores for individual image regions. One of the main problems with 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 study, we investigate a multi-scale analysis approach which 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 analysis and improve the tampering localization performance.