AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Kadam, Kalyani; Ahirrao, Swati; Kotecha, Ketan

doi:10.11591/ijece.v11i5.pp4489-4501

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Table 1 shows the specifications of the Multiple Image Splicing Dataset. Therefore, there is an urgent need for efficacious Forgery Detection techniques [8][9][10]. To validate these techniques, standard datasets are required.…”

Section: Discussionmentioning

confidence: 99%

Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Kadam

Ahirrao

Kotecha

2021

Data

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Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Carvalho, and CASIA V1.0. However, these datasets are employed for the detection of image splicing. There are also a few custom datasets available such as Modified CASIA, AbhAS, which are also employed for the detection of image splicing forgeries. A study of existing datasets used for the detection of image splicing reveals that they are limited to only image splicing and do not contain multiple spliced images. This research work presents a Multiple Image Splicing Dataset, which consists of a total of 300 multiple spliced images. We are the pioneer in developing the first publicly available Multiple Image Splicing Dataset containing high-quality, annotated, realistic multiple spliced images. In addition, we are providing a ground truth mask for these images. This dataset will open up opportunities for researchers working in this significant area.

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Section: Discussionmentioning

confidence: 99%

Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Kadam

Ahirrao

Kotecha

2021

Data

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“…Various image copy-move forgery detection algorithms based on convolutional neural network (CNN) and deep learning are evaluated and conluded that CNN based image copy-move forgery detection (IC-MFD) algorithms are more effective, less time consuming and uses up less reaources too [20]. A broad literature review is presented in [21] on image forgery detection with deep learning approach which have accomplished high performance accuracies incontext with image or facial image. Research by Ahmed et al [22] focuses on SFTA, Haralick, and LBP as feature extraction methods followed by features fed to KNN classifier to detect and classify the copy-move attack.…”

Section: Classification Of Forgery Detectionmentioning

confidence: 99%

A comparative study on image forgery-facial retouching

Sheth

Vora

2023

Bulletin EEI

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Forgery with the digital images is being very easy now days due to the very advanced and open source image editing tools, software and devises which supports a high quality of resolutions. Tempering with digital documents for changing identity or sometimes for fun is increasing day by day as the era is of digital world. Detecting clues of tampering and verifying the authenticity of images is an important issues now-a-days and growing research field. The existing research in the area of digital image forgery identification is discussed here. Different types of image forgery attacks along with its detection and classification are discussed too. A general review of facial image authentication is studied and discussed here along with the current methods and techniques for the detection and classification. A comparative analysis is presented here for the researchers as a mind mapping to work in the same field.

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“…On the other hand, from a bibliometric study, the author noticed that universities and institutes in the United States of America highly contribute to the field of image forgery detection by considering deep learning and artificial intelligence mechanisms. During their analysis of the Scopus database, they found this area as an emerging field that got the attention of academic researchers across the globe (Kadam and Ahirrao, 2020). Considering any database like Web of Science (WoS) or Scopus (Elsevier), bibliometric analysis was carried out to determine the emerging trends in a specific field and also predicted which trends would be highly investigated in future eras (Zhang et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Four decades of image processing: a bibliometric analysis

Khan

Khan²,

Iqbal³

et al. 2022

LHT

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PurposeImage Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.Design/methodology/approachThe Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.FindingsThe research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.Originality/valueThis research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

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AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Cited by 9 publications

References 21 publications

Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

A comparative study on image forgery-facial retouching

Four decades of image processing: a bibliometric analysis

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