Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 25, Issue 3, Pages 1312-1326Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2518870
Keywords
Digital image forensics; tampering localization; result fusion; multi-scale analysis; first-digit-features; energyminimization; Markov random fields
Funding
- National Natural Science Foundation of China [61332012, 61572329, 61402295]
- Guangdong National Science Foundation [2014A030313557]
- Shenzhen Research and Development Program [GJHZ20140418191518323]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available