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 CLIFD: A novel image forgery detection technique using digital signatures

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sahib Khan ◽  
◽  
Arslan Ali ◽  
Keyword(s):  
Digital Signature ◽  
Digital Signatures ◽  
Detection Technique ◽  
Forgery Detection ◽  
Image Forgery ◽  
Signature Generation ◽  
Image Forgery Detection ◽  
Substitution Mechanism ◽  
Different Types ◽  
Message Digest

The paper presents a new image forgery detection technique. The proposed technique uses digital signatures; it generates a digital signature for each column and embeds the signature in the least significant bits of each corresponding column’s selected pixels. The message digest algorithm 5 (MD5) is used for digital signature generation, and the fourleast-significant-bit substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.

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 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.

scholarly journals Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Hameed Siddiqi ◽  
Khurshed Asghar ◽  
Umar Draz ◽  
Amjad Ali ◽  
Madallah Alruwaili ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Digital Images ◽  
Local Binary Patterns ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Image Forgery Detection

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

scholarly journals Image Forgery Detection Based on Deep Transfer Learning

2019 ◽  
Vol 3 (5) ◽  
Author(s):  
Younis Abdalla ◽  
M. T. Iqbal ◽  
M. Shehata
Keyword(s):  
Transfer Learning ◽  
Feature Space ◽  
Training Image ◽  
Training Data ◽  
Space Distribution ◽  
Forgery Detection ◽  
Image Forgery ◽  
Digital Revolution ◽  
Image Forgery Detection ◽  
Novel Approach

The recent digital revolution has sparked a growing interest in applying convolutional neural networks (CNNs) and deep learning to the field of image forensics. The proposed methods aim to train algorithms for solving a range of predetermined tasks. However, training a model that has been randomly initialized requires extensive time for computation as well as an enormous pool of training data to draw from. Moreover, such a model needs to be developed and redeveloped from the ground up if there are any alterations to the feature-space distribution. In addressing these problems, the present paper proposes a novel approach to training image forgery detection models. The method applies prior knowledge that has been transferred to the new model from previous steganalysis models. Additionally, because CNN models generally perform badly when transferred to other databases, transfer learning accomplished through knowledge transfer allows the model to be easily trained for other databases. The various models are then evaluated using image forgery techniques such as shearing, rotating, and scaling images. The experimental results, which show an image manipulation detection has validation accuracy of over 94.89%, indicate that the proposed transfer learning approach successfully accelerates CNN model convergence but does not improve image quality.     

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