**2. Copy-move forgery detecting**

The ease and effectiveness of counterfeiting facilitates its application in changing image content [11]. The important features, like the pallet and the active range, of replicated areas are compatible with the rest of the image given that these areas are obtained from the same image [12]. Nevertheless, in practice, counterfeiting may imply more than simple replication. Numerous image-editing processes may be applied in serious counterfeiting, as shown in **Figure 3**. The processes can be divided into two groups: intermediator processes and postprocesses. Intermediator processes are applied to synchronicity and homogeneity between a replicated region and its neighbor [13]. Intermediator processes include rotation, scaling,

**Figure 3.** Image processing operations associated with image forgery.

reflection, lighting adjustment, or color adjustment. In serious cases, intermediator processes can be combined. Postprocesses, such as noise addition, joint photographic expert group (JPEG) compression, or blurring, can be applied to delete all retraces that can be detected in the copy process, such as sharp edges [2]. A broad range of easily available algorithms has been proposed to detect replicated images and functions, as shown in **Figure 4**.

To detect image forgery, an image is first selected (e.g., converted to gray scale). The image is divided into an auction block of nested pixels. The size of the image *m\_n*, size of block *B*, and the number of overlapping blocks is given by:

$$\text{No of blocks} = (m - k + 1) \times (n - k + 1) \tag{1}$$

**3. Copy-move detecting algorithm**

**3.1. Algorithm based on invariant keypoints**

but otherwise poor performance.

*3.1.1. SIFT algorithm*

presented.

Numerous articles on the negative detection of displacement in images have been and continue to be published. Existing methods for displacement detection are primarily distinguished on the basis of the case and sizing of the function applied to match the image block. This article classifies existing methods in accordance with the extracted properties applied to test block similarities. In the following sections, different cases or classes of detection algorithms are

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

http://dx.doi.org/10.5772/intechopen.76332

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In contrast to other algorithms, this algorithm does not divide the image into auction blocks to extract features but instead extracts features from the intact image. Feature extraction is performed with SIFT and speeded-up robust feature (SURF). This technique is applied to derive the characteristic local feature of an image and produce a keypoint in accordance with preset requirements. The vector sum/descry values are fixed for rotational, translational, and scale measurements and are partially fixed for strong illumination changes in local geometric distortion [14, 15]. The first attempt to exploit this algorithm was reported by [16]. In the algorithms, only the correspondence of the keypoint can be achieved by its maximum bin, including the identity of the nearest neighbor [17]. SIFT has been adopted to identify replicated regions in a counterfeit image. The SIFT signifier is applied to detect copied areas by coping with keypoints rather than clusters. This algorithm has excellent detection accuracy

He proposed the SIFT algorithm, which could be used to detect and evaluate the geometrical shifts applied to forged displacement copy-and-paste images. The detection procedure involves three steps: In the first step, SIFT functions are extracted and main points are associated. The second step is committed to keypoint compilation and fraud detection. The third step estimates the engineering shifts, if any, that occurred. SIFT can be executed under the conditions of eminent real rate (TPR) and abject fake positive degree ratio (FRE), JPEG compression, and additional noise. In addition, SIFT can accurately estimate different arguments

for affine transmutation. **Figure 5** shows different arguments for affine transmutation.

The first attempts to take advantage of SIFT have been reported in [16]. In SIFT, the correspondence of the key indicator is achieved by first identifying the neighbor closest to the best bin [17]. SIFT has been adopted to identify a single copy in the counterfeit image. SIFT descriptors are usually applied to identify keypoints of copied areas instead of blocks, whereas other algorithms cope with object indicators. Although SIFT exhibits excellent detection performance, its false–positive rate remains unknown. In [18], the main SIFT points were extracted from the image and were then associated to obtain the corresponding keypoints. A vote scheme based on vector direction was applied to distinguish between origin and direction. Then, an

The vector is an extractable characteristic in each block. The vector-matching function is highly similar to pairing functions. Known pairing methods include the arrangement of miracle dictionaries on the element vectors and the identification of the nearest neighbor in the tree *Kd*. The similarity between two attributes can be determined on the basis of similarities between different parameters, such as Euclidean length. In the verification step, extreme values are suppressed and holes are filled up through a basic filtration step, such as morphing.

**Figure 4.** Pipeline of a fraud detection algorithm.
