Comparative Study of Various Forgery Detection Approach for Image Processing

2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Manish KUMARI ◽  
Rajesh SHARMA
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Comparative Study ◽  
Digital Images ◽  
Real Life ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Approach

Considering the availability of powerful image analysis and editing tools, digital images are easy to change and transfer. This is necessary to link or erase any important elements from any image without escaping any valid visible signs of interfering. Including its real-life apps in different areas, the copy move forgery method is analyzed in depth. Implementation phases for the detection of image forgery are also clarified, accompanied by various approaches using copy move forgery approach.

Integrating Image Computation in Undergraduate Level Data-Structure Education

1998 ◽  
Vol 12 (08) ◽  
pp. 1071-1080 ◽  
Author(s):  
Sudeep Sarkar ◽  
Dmitry Goldgof
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Data Structure ◽  
Data Structures ◽  
Science Curriculum ◽  
Early Stage ◽  
Real Life ◽  
South Florida ◽  
The University ◽  
Structure Education

There is a growing need for expertise both in image analysis and in software engineering. To date, these two areas have been taught separately in an undergraduate computer and information science curriculum. However, we have found that introduction to image analysis can be easily integrated in data-structure courses without detracting from the original goal of teaching data structures. Some of the image processing tasks offer a natural way to introduce basic data structures such as arrays, queues, stacks, trees and hash tables. Not only does this integrated strategy expose the students to image related manipulations at an early stage of the curriculum but it also imparts cohesiveness to the data-structure assignments and brings them closer to real life. In this paper we present a set of programming assignments that integrates undergraduate data-structure education with image processing tasks. These assignments can be incorporated in existing data-structure courses with low time and software overheads. We have used these assignment sets thrice: once in a 10-week duration data-structure course at the University of California, Santa Barbara and the other two times in 15-week duration courses at the University of South Florida, Tampa.

scholarly journals A Review Paper on Image Forgery Detection In Image Processing

2016 ◽  
Vol 18 (04) ◽  
pp. 86-89 ◽  
Author(s):  
Shivani Thakur ◽  
Ramanpreet Kaur ◽  
Dr. Raman Chadha ◽  
Jasmeet Kaur
Keyword(s):  
Image Processing ◽  
Review Paper ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

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 Forensic Science Research Summary for Forgery Detection of Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1608-1618
Keyword(s):  
Image Processing ◽  
Forensic Science ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Science Research ◽  
Forensic Sciences ◽  
Forgery Detection ◽  
New Approach ◽  
Scanned Images

An important measure of proof collection, storage, and authentication in forensic sciences, which decide the safety and security of any system documents, which can be either portable document formats or scanned images. To gather evidence, or plan a forensic investigation digital images are secured with different modern methodologies. Digital image analysis includes image recovery and surveillance for image information improvement. The goal of forgery detection is to maximize the extraction of information from manipulated images, particularly noisy and post-processed images. Because digital image processing is becoming popular with many advantages in scientific and engineering applications, the forgery techniques are also growing at a rapid rate. Therefore, the main focus is on different types of forgery detection in digital image processing with the help of all transform techniques and comparing their best results for further improvement in order to generate a new approach for a future forensic science investigation.

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 Digital Image Forgery Detection

2019 ◽  
Vol 8 (2S3) ◽  
pp. 658-661
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Image Manipulation ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Naked Eye ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Digital Image Forgery

With the growing challenges in authenticity and integrity of images, image manipulation has crumbled assurance over digital image. The major motivation of the forgery in image is manipulating the image in such a way that it cannot be distinguished to the naked eye. Image manipulation has increased the demand to assess the trustworthiness of digital images when used in crime investigation, as witness of law and for surveillance purposes. In this paper, various types of image forgery and detection techniques have been explained. Initially different kinds of forgery attacks are categorized and summary of passive approach is discussed

scholarly journals Copy Move Image Forgery Detection with Exact Match Block Based Technique

2019 ◽  
Vol 12 (Issue 3) ◽  
pp. 123-131
Author(s):  
Priyanka Arora ◽  
Derminder Singh
Keyword(s):  
Execution Time ◽  
Digital Images ◽  
Block Size ◽  
Forgery Detection ◽  
Exact Match ◽  
Image Forgery ◽  
File Formats ◽  
The Common ◽  
Block Based ◽  
Block Sizes

Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16. It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.

A Markov based image forgery detection approach by analyzing CFA artifacts

2018 ◽  
Vol 77 (21) ◽  
pp. 28949-28968 ◽  
Author(s):  
Amneet Singh ◽  
Gurinder Singh ◽  
Kulbir Singh
Keyword(s):  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Detection Approach
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