Seam Carving Detection and Localization using Two-Stage Deep Neural Networks

TL;DR

This paper introduces a two-stage deep learning approach to detect and localize seam carving in images, improving content-aware image manipulation detection.

Contribution

It presents a novel two-step neural network framework for detecting seam carving and localizing manipulated regions in images.

Findings

Effective detection of seam carved images.

Accurate localization of carved regions.

High detection precision in experiments.

Abstract

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.