Seam Carving Detection and Localization using Two-Stage Deep Neural Networks
This addresses image forensics and security issues for digital media analysts, but it is incremental as it builds on existing seam carving detection techniques.
The paper tackles the problem of detecting and localizing seam carving in images, which can be used to remove objects, and proposes a two-stage deep neural network method that effectively identifies manipulated images.
Seam carving is a method to resize an image in a content aware fashion. However, this method can also be used to carve out objects from images. In this paper, we propose a two-step method to detect and localize seam carved images. First, we build a detector to detect small patches in an image that has been seam carved. Next, we compute a heatmap on an image based on the patch detector's output. Using these heatmaps, we build another detector to detect if a whole image is seam carved or not. Our experimental results show that our approach is effective in detecting and localizing seam carved images.