On rotation invariance in copy-move forgery detection

The goal of copy-move forgery detection is to find duplicated regions within the same image. Copy-move detection algorithms operate roughly as follows: extract blockwise feature vectors, find similar feature vectors, and select feature pairs that share highly similar shift vectors. This selection plays an important role in the suppression of false matches. However, when the copied region is additionally rotated or scaled, shift vectors are no longer the most appropriate selection technique. In this paper, we present a rotation-invariant selection method, which we call Same Affine Transformation Selection (SATS). It shares the benefits of the shift vectors at an only slightly increased computational cost. As a byproduct, the proposed method explicitly recovers the parameters of the affine transformation applied to the copied region. We evaluate our approach on three recently proposed feature sets. Our experiments on ground truth data show that SATS outperforms shift vectors when the copied region is rotated, independent of the size of the image.