Hybrid deep learning and machine learning approach for passive image forensic

Thakur, Abhishek; Jindal, Neeru

doi:10.1049/iet-ipr.2019.1291

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…Our system can result in inappropriate reading due to the atmospheric conditions outside the system, like scorching weather, which may result in an inevitable rise in individual assignments [11][12][13][14].…”

Section: Drawbacksmentioning

confidence: 99%

Role of IoT to Avoid Spreading of Covid19

Abhishek

Ranjan

Kaur

2023

Advances in Transdisciplinary Engineering

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To determine the capabilities of this technology, we refer to different research papers related to this topic. This literature-based research could assist practitioners in devising responses to relevant issues and combating the COVID-19 pandemic. This paper examines the position of IoT-based technology in COVID-19. It looks at state-of-the-art architectures, networks, implementations, and industrial IoT-based solutions for combating COVID-19 in three stages: early detection, quarantine, and recovery. Since 2020 was a challenging year for all of us, and during this pandemic, we all realized that social gatherings had to be avoided, and the serious issue was to handle it. So to tackle this and ease the handling of the Corona Virus, we developed an automatic door that monitors an individual’s temperature and whether the person is wearing a mask. In the absence of a mask, it clicks a picture of the person and stores it in the database for future reference.

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Section: Drawbacksmentioning

confidence: 99%

Role of IoT to Avoid Spreading of Covid19

Abhishek

Ranjan

Kaur

2023

Advances in Transdisciplinary Engineering

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“…For the identification and localisation of splicing image forgeries, (Griebenow, 2017) (Thakur & Jindal, 2020) proposed a hybrid deep learning (DL) and machine learning-based approach to detect copy move and splicing forgery. The forgery is detected using deep learning technique and the localization of the forgery is done using Machine Learning based colour illumination algorithm.…”

Section: Related Workmentioning

confidence: 99%

Effect of Image Splicing and Copy Move Forgery on Haralick Features of a Digital Image

Sulaiman¹,

Bagiwa²

2022

SLUJST

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Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem. The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in thisexperiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated bya copy move forgery

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“…However, the forensic investigation has identified the tigers termed as the "paper tigers" [3]. In the same way, in 2008, official images of four Iranian ballistic weapons were proved to be forged as one of the missiles was exposed to be counterfeited [4]. Hence, methods that could guarantee the integrity of images particularly from the evidence centred application are thus required.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

Kumar¹,

Sundaram²

2023

Intelligent Automation &Amp; Soft Computing

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The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered, and then localize the copy moved regions. To perform the feature extraction process, the political optimizer (PO) with Mobile Networks (MobileNet) model has been derived for generating a set of useful vectors. Finally, an enhanced bird swarm algorithm (EBSA) with least square support vector machine (LS-SVM) model has been employed for classifying the digital images into the original or the forged ones. The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine (MSVM) technique and thereby enhance the classification performance. For ensuring the enhanced performance of the ODTL-CMFD technique, a series of simulations have been performed against the benchmark MICC-F220, MICC-F2000, and MICC-F600 datasets. The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

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Hybrid deep learning and machine learning approach for passive image forensic

Cited by 16 publications

References 31 publications

Role of IoT to Avoid Spreading of Covid19

Role of IoT to Avoid Spreading of Covid19

Effect of Image Splicing and Copy Move Forgery on Haralick Features of a Digital Image

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

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