Automated Forensic Method for CopyMove Forgery Detection based on Harris Interest Points and SIFT Descriptors

Abstract

We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. Nowadays, accepting digital images of official documents is common practice. Image authenticity is important in many social areas. For instance, the trustworthiness of photographs has an essential role in courtrooms, where they are used as evidence. In the medical field, physicians make critical decisions based on digital images. The technology today makes it convenient to quickly exchange contracts, photographs or other documents. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. It is possible to change the information represented by an image and create forgeries, which are indistinguishable by naked eye from authentic photographs and documents. In the proposed method Harris Interest Point detector along with SIFT descriptors are used to detect copymove forgery. KD-Tree is used for matching.

Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation

“…There are many feature extraction methods used in forgery detecting and locating. Based on the previous studies, the three most effective methods used for extracting good features to trace are: Harris [ 33 , 34 ], Gray Level Co-occurrence Matrix (GLCM) [ 6 ] and Singular Value Decomposition (SVD) [ 22 ], In this paper, each of which is applied for 2D matrix, tested and compared to obtain the best combination.…”

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

“…Furthermore, affine transformation is exactly estimated, particularly in larger duplicated areas. A different scenario is to integrate SIFT into copy detection systems [22]. Instead of applying SIFT to detect keypoints, the Harris quicker from SIFT is applied.…”

“…Then, the Kd trees algorithms are applied to match the keypoints to identify duplicated areas. The algorithms can effectively detect copied areas, such as unrotated scanlines or Gaussian noise conditions, that have undergone transformation [5,22]. Harris detection, which is quicker than SIFT, has been used to detect keypoints.…”

A review on copy move image forgery detection techniques

“…Features are extracted only for keypoints hence increasing computational efficiency. In[29]-[31], the Scale-Invariant Feature Transform (SIFT) features were used while the SURF approach was discussed in[31]. SIFT is used to describe local features in an image and is efficient in detecting duplicate regions even if the regions undergo transformations like scaling and rotation and is robust to noise and changes in illumination conditions.…”