Copy-Move Forgery Detection via Dimensionality Reduction and Double Quantization Feature

Abstract

Copy-move forgery detection is a crucial task in digital image forensics, aiming to identify duplicated regions within an image. Traditional methods often locate the forged regions but struggle to distinguish between the original and copied areas. We introduce a new method that addresses this issue. Initially, the method extracts the low-frequency components of the image using contourlet transform. These components are then divided into overlapping blocks, and singular value features are extracted from each block. The feature vectors are sorted lexicographically and combined with the offset information of the image blocks to identify suspicious regions. To further refine the detection, double quantization effect feature is computed for blocks within these suspicious regions. When the double quantization feature value of a block exceeds a certain threshold, it is classified as part of the copied region. Experimental results demonstrate that the proposed method not only effectively detects and localizes copy-move forgeries but also accurately identifies the original and copied regions. Moreover, the method proves to be effective even on natural images containing multiple similar-but-genuine objects, reducing the false alarm rate.