Image Forensics Based on Lighting Estimation

Kumar, Manoj; Rani, Anuj; Srivastava, Sangeet

doi:10.1142/s0219467819500141

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…They devise a method for evaluating the side of a lighting source in the first degree of freedom to demonstrate the effects of tampering. Kumar et al [ 53 ] proposed an approach that works for photographs with any sort of item present in the scene, i.e. it is not restricted to human faces and image selection of same intensity regions.…”

Section: Experimentationmentioning

confidence: 99%

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Sharma

Kumar²,

Sharma³

2022

Multimed Tools Appl

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The digital image proves critical evidence in the fields like forensic investigation, criminal investigation, intelligence systems, medical imaging, insurance claims, and journalism to name a few. Images are an authentic source of information on the internet and social media. But, using easily available software or editing tools such as Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator, etc. images can be altered or utilized maliciously for personal benefits. Various active, passive and other new deep learning technology like GAN approaches have made photo-realistic images difficult to distinguish from real images. Digital image tamper detection now focuses on determining the authenticity and consistency of digital photos. The major research problems use generic solutions and strategies, such as standardized data sets, benchmarks, evaluation criteria and generalized approaches.This paper overviews the evaluation of various image tamper detection methods. A brief discussion of image datasets and a comparative study of image criminological (forensic) methods are included in this paper. Furthermore, recently developed deep learning techniques along with their limitations have also been addressed. This study aims to comprehensively analyze image forgery detection methods using conventional and advanced deep learning approaches.

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Section: Experimentationmentioning

confidence: 99%

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Sharma

Kumar²,

Sharma³

2022

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

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“…The outcome of applying the lighting estimation technique reveals that the method is only marginally improved by excluding specific observations, such as geometry depending on shadowing and upgrade and improve characteristics on selected input images [13]. The technique to identify image modifications is approved by the publication in consideration of the abnormal illumination.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm

Zainal¹,

Kaur²,

Ansari³

et al. 2022

IJACSA

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In the modern day, protecting data against tampering is a significant task. One of the most common forms of information display has been digital photographs. Images may be exploited in a variety of contexts, including the military, security applications, intelligence areas, legal evidence, social media, and journalism. Digital picture forgeries involve altering the original images with strange patterns, which result in variability in the image's characteristics. Among the most challenging forms of image forgeries to identify is Copy Move Forgery (CMF). It occurs by copying a portion or piece of the picture and then inserting it again, but in a different place. When the actual content is unavailable, techniques for detecting fake content have been utilised in image security. This study presents a novel method for Copy Move Forgery Recognition (CMFR), which is mostly based on deep learning (DL) and hybrid optimization. The hybrid Grey Wolf Optimization and African Buffalo Optimization (GWO-ABO) using Convolution Neural Network (CNN) technique i.e., GWO-ABO-CNN is the foundation of the suggested model. The developed model extracts the features of images by convolution layers, and pooling layers; hereafter, the features are matched and detect CMF. The MICC-F220, SATs-130, and MICC-F600 datasets were three publicly accessible datasets to which this methodology has been implemented. To assess the model's efficacy, the outcomes of implementing the GWO-ABO-CNN model were contrasted with those of other approaches.

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“…Some methods designed to detect such attacks are presented in [45,46]. Another approach that is used to validate document image source is estimation and control of lighting [47].…”

Section: Fig 3 General Scheme Of the Identity Document Recognition Pi...mentioning

confidence: 99%

Towards a unified framework for identity documents analysis and recognition

Bulatov¹,

Bezmaternykh²,

Nikolaev

et al. 2022

Computer Optics

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Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.

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Image Forensics Based on Lighting Estimation

Cited by 16 publications

References 9 publications

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm

Towards a unified framework for identity documents analysis and recognition

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