RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

2019 ◽  
Author(s):  
Xiuli Bi ◽  
Yang Wei ◽  
Bin Xiao ◽  
Weisheng Li
Keyword(s):  
Forgery Detection ◽  
Image Splicing

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 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.

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 Reviewing the Effectivity Factor in Existing Techniques of Image Forensics

2017 ◽  
Vol 7 (6) ◽  
pp. 3558
Author(s):  
Shashidhar TM ◽  
KB Ramesh
Keyword(s):  
Image Forensics ◽  
Research Trend ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Detection Techniques ◽  
Open Research ◽  
Image Forgery Detection ◽  
Attack Scenario ◽  
Research Gap

Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.

Image Forgery Detection and Localization

2021 ◽  
pp. 176-182
Author(s):  
Aditi Shedge ◽  
Shaily Shah ◽  
Shubham Pandey ◽  
Mansi Pandey ◽  
Rupali Satpute
Keyword(s):  
Human Brain ◽  
Sources Of Information ◽  
Forgery Detection ◽  
Image Splicing ◽  
Tampering Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Detection And Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

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