Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
This work addresses digital image forensics for security and verification purposes, but it appears incremental as it builds on existing resampling and deep learning techniques.
The paper tackled the problem of detecting and localizing image forgeries by combining resampling features with deep learning, resulting in two effective methods that show promising performance in experiments.
Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.