Wavelet based inpainting detection

TL;DR

This paper presents a novel inpainting forgery detection method combining DT-CWT, hierarchical segmentation, and noise analysis, demonstrating superior accuracy over existing techniques on a benchmark dataset.

Contribution

It introduces a new approach leveraging DT-CWT's shift-invariance and directional selectivity for more effective inpainting forgery detection.

Findings

Outperforms state-of-the-art methods in detection accuracy

Robust to minor manipulations due to shift-invariance

Effective in identifying subtle inpainting artifacts

Abstract

With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting in specific frequency bands and orientations. By first applying color image segmentation and then analyzing for each segment, noise inconsistency obtained via DT-CW we can identify patterns indicative of inpainting forgeries. The proposed method is evaluated on a benchmark dataset created for this purpose and is compared with existing forgery detection techniques. Our approach demonstrates superior results compared with SOTA in detecting inpainted images.