Copy-move Forgery Detection Robust to Various Transformation and Degradation Attacks

Abstract

Trying to deal with the problem of low robustness of Copy-Move Forgery Detection (CMFD) under various transformation and degradation attacks, a novel CMFD method is proposed in this paper. The main advantages of proposed work include: (1) Discrete Analytical Fourier-Mellin Transform (DAFMT) and Locality Sensitive Hashing (LSH) are combined to extract the block features and detect the potential copy-move pairs; (2) The Euclidian distance is incorporated in the pixel variance to filter out the false potential copy-move pairs in the post-verification step. In addition to extracting the effective features of an image block, the DAMFT has the properties of rotation and scale invariance. Unlike the traditional lexicographic sorting method, LSH is robust to the degradations of Gaussian noise and JEPG compression. Because most of the false copy-move pairs locate closely to each other in the spatial domain or are in the homogeneous regions, the Euclidian distance and pixel variance are employed in the post-verification step. After evaluating the proposed method by the precision-recall-F 1 model quantitatively based on the Image Manipulation Dataset (IMD) and Copy-Move Hard Dataset (CMHD), our method outperforms Emam et al.'s and Li et al.'s works in the recall and F 1 aspects.

Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)

“…This section compares the identified results with some of the advanced CMFD methods on the MICC-F600 dataset. The methods of introduction in this section, as in the previous section, are SIFT [15], KAZE [20], PCET [22], MSA [24], BAM [23], and DAMFT [25]. In this case, the results are shown in the simple copy subset in Table 2.…”