scholarly journals An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 706 ◽  
Author(s):  
Chengyou Wang ◽  
Zhi Zhang ◽  
Xiao Zhou
Keyword(s):  
Feature Detection ◽  
Joint Photographic Expert Group ◽  
Social Stability ◽  
Forgery Detection ◽  
Detection Scheme ◽  
Speeded Up Robust Features ◽  
Image Blurring ◽  
Surf Features ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

Solving Time Complexity Issue in Copy-Move Forgery Detection Thru Pre-processing Techniques

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Siti Fadzlun Md Salleh ◽  
Mohd Foad Rohani ◽  
Mohd Aizaini Maarof Maarof
Keyword(s):  
Time Complexity ◽  
Processing Technique ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Complexity Issue ◽  
Processing Step ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.

Single and multiple regions duplication detections in digital images with applications in image forensic

2021 ◽  
Vol 40 (6) ◽  
pp. 10351-10371
Author(s):  
Marriam Nawaz ◽  
Zahid Mehmood ◽  
Muhammad Bilal ◽  
Asmaa Mahdi Munshi ◽  
Muhammad Rashid ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Input Image ◽  
Image Manipulation ◽  
Image Editing ◽  
Post Processing ◽  
Speeded Up Robust Features ◽  
Multiple Regions ◽  
Surf Features ◽  
Copy Move Forgery Detection

‘With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.

scholarly journals A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

2018 ◽  
Vol 7 (3) ◽  
pp. 345-349
Author(s):  
Anil Gupta
Keyword(s):  
Digital Image ◽  
Feature Point ◽  
Detection Methods ◽  
Forgery Detection ◽  
Point Matching ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Feature Point Matching ◽  
Copy Move Forgery Detection

With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

Copy-Move Forgery Detection Based on Euclidean Distance and Texture Feature Analysis

2021 ◽  
Vol 2 (2) ◽  
pp. 25-32
Author(s):  
Ashutosh Kumara ◽  
Neha Janu
Keyword(s):  
Euclidean Distance ◽  
Feature Detection ◽  
Feature Matching ◽  
Texture Feature ◽  
Input Image ◽  
Feature Analysis ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Pca Algorithm

Digital images are important part of our life. Copy and Move forgery detection techniques are designed to detect edited part of the image. The copy and move forgery techniques are based on the feature detection and matching. The techniques which are designed so far use the Euclidean distance concept for feature matching. The feature detection techniques which are much popular like Haar transformation are used for feature extraction. In this research, the PCA algorithm is used for the simplification of features which are extracted with Haar transformation. The GLCM algorithm is used for texture feature analysis of input image. In the end, Euclidean distance is used for feature matching and mismatched features are marked as forgery. The proposed approach is implemented in MALTAB and results are analyzed in terms of accuracy.

Segmentation-Based Image Copy-Move Forgery Detection Scheme

2015 ◽  
Vol 10 (3) ◽  
pp. 507-518 ◽  
Author(s):  
Jian Li ◽  
Xiaolong Li ◽  
Bin Yang ◽  
Xingming Sun
Keyword(s):  
Forgery Detection ◽  
Detection Scheme ◽  
Copy Move Forgery Detection
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