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 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 Forgery Detection and Localization of Modifications at the Pixel Level

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 137 ◽  
Author(s):  
Sahib Khan ◽  
Khalil Khan ◽  
Farman Ali ◽  
Kyung-Sup Kwak
Keyword(s):  
Digital Signature ◽  
Digital Signatures ◽  
New Technique ◽  
Forgery Detection ◽  
Hash Algorithm ◽  
Message Digest ◽  
A New Technique ◽  
Detection And Localization ◽  
Least Significant Bits ◽  
Made In

In this paper, we present a new technique of image forgery detection. The proposed technique uses digital signatures embedded in the least significant bits of the selected pixels of each row and column. The process maintains a symmetry in the use of pixels for computing and hiding the digital signatures. Each row and column of the image symmetrically contributes to both processes, with the number of pixels per row or column used for computing the signature, and the pixels used for embedding are not equal and are asymmetric. The pixels in each row and column of an image are divided into two groups. One group contains pixels of a row or column used in the calculation of digital signatures, and the second group of pixels is used for embedding the digital signatures of the respective row or column. The digital signatures are computed using the hash algorithm, e.g., message digest five (MD5). The least significant bits substitution technique is used for embedding the computed digital signature in the least significant bits of the selected pixels of the corresponding row or column. The proposed technique can successfully detect the modification made in an image. The technique detects pixel level modification in a single or multiple pixels.

Image Forgery Detection Based on Trigonometric Transforms

2019 ◽  
Vol 28 (2) ◽  
pp. 203-216
Author(s):  
Faten Al-Azrak ◽  
Moawad Dessouky ◽  
Fathi Abd El-Samie ◽  
Ahmed Elkorany ◽  
Zeinab Elsharkawy
Keyword(s):  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Trigonometric Transforms
Sign in / Sign up

Export Citation Format

Share Document