scholarly journals Copy-Move Image Forgery Detection using Adhoc Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 4425-4429
Keyword(s):  
Block Size ◽  
Detection Methods ◽  
Similarity Function ◽  
Original Image ◽  
Forgery Detection ◽  
Image Splicing ◽  
Passive Detection ◽  
Forged Parts ◽  
Tampered Image ◽  
The Given

It is easy make fake images by making use of editing software. It has become an effortless job to put together or detach some attributes from an image. Validation of digital images is very essential. Identifying the fake image is the crucial topic. In order to identify tampered image active and passive detection methods are used. An image can be tampered by using image splicing, copy-move, and retouching. In particular, copy-move attack is considered in this paper. It is essential to find out whether the image is tampered or not. An effective method for detecting forged image is proposed which uses adhoc algorithm. In this algorithm there is no need of original image as it compares the similar pixels in the given image. Clusters which are larger than block size are pulled out. Similar clusters are extracted using some similarity function. The method successfully detects the forged parts in the image and saves the forged image in JPEG format. The performance is measured on various images.

scholarly journals A Survey on Photo Forgery Detection Methods

2018 ◽  
Vol 22 ◽  
pp. 01055
Author(s):  
Bilgehan Gurunlu ◽  
Serkan Ozturk
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Detection Methods ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Detection Techniques ◽  
Passive Detection ◽  
Image Forgery Detection ◽  
Digital Image Forgery

In recent years, digital image forgery detection has become one of the hardest studying area for researchers investigations in the field of information security and image processing. Image forgery detection methods can be divided into two extensive groups such as Active methods and Passive (Blind) methods. Active methods have been used data hiding techniques like watermarking and digital signatures. Passive forensic methods (or Blind) use image statistics or they investigate the attributes of the image to determine the forgeries. Passive detection techniques are also split into three branches; image splicing, image retouching, copy-move. Such image forgery detection methods are focus of this paper.

Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

2020 ◽  
Vol 12 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Nadheer Younus Hussien ◽  
Rasha O. Mahmoud ◽  
Hala Helmi Zayed
Keyword(s):  
Digital Image ◽  
Feature Reduction ◽  
Color Filter ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Digital Image Forgery

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

scholarly journals Survey on Keypoint Based Copy-move Forgery Detection Methods on Image

2016 ◽  
Vol 85 ◽  
pp. 206-212 ◽  
Author(s):  
Devanshi Chauhan ◽  
Dipali Kasat ◽  
Sanjeev Jain ◽  
Vilas Thakare
Keyword(s):  
Detection Methods ◽  
Forgery Detection ◽  
Copy Move Forgery Detection

scholarly journals Image De-Blurring Based on Constraint Conditional Model

2020 ◽  
Vol 9 (3) ◽  
pp. 995-1000
Keyword(s):  
The Other ◽  
Original Image ◽  
Conditional Model ◽  
Proposed Model ◽  
Image Capturing ◽  
Degraded Image ◽  
Physical Limitations ◽  
Blur Kernel ◽  
The Given ◽  
State Of Art

Image capturing is more vulnerable to the various physical limitations such as defocus, low lighting and camera shaking; this makes the image blurry and noisy. Moreover De-blurring is the process to recover the original image from the given degraded image. De-blurring technique uses the estimated blur kernel for achieving the optimal restored image with the sharp features, however the accuracy has been one of the major concern , hence in this paper we use Constrained Conditional model (CCM) for restoring the image. Moreover, here two different methods are integrated i.e. conditional model and convergence operator, these two combined learns the model and efficiently and provides the better results. In order to evaluate the proposed model, Levin dataset is used by considering the two eminent model metric i.e. PSNR and SSIM and CCM based model outperforms the other state-of-art technique.

An Overview on Passive Image Forensics Technology for Automatic Computer Forgery

2020 ◽  
pp. 509-520
Author(s):  
Jie Zhao ◽  
Qiuzi Wang ◽  
Jichang Guo ◽  
Lin Gao ◽  
Fusheng Yang
Keyword(s):  
Image Forensics ◽  
Research Interest ◽  
Detection Methods ◽  
Comprehensive Overview ◽  
Intelligent Algorithms ◽  
Automatic Computer ◽  
Photographic Images ◽  
Passive Detection ◽  
Detection Technology ◽  
Passive Image Forensics

Currently, with the popularity of sophisticated image editing tools like Photoshop, it is becoming very difficult to discriminate between an authentic image and its manipulated version, which poses a serious social problem of debasing the credibility of photographic images as definite records of events. Passive image forgery detection technology, as one main branch of image forensics, has been regarded as the promising research interest due to its versatility and universality. Automatic computer forgery employs computer intelligent algorithms to forge an image in an automatic way, which is rather more complex than copy-move forgery since the source of duplicated region could be non-continuous. In this paper, the authors provide a comprehensive overview of the state-of-the-art passive detection methods for automatic computer forgery.

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