Recent Advances in Passive Digital Image Security Forensics: A Brief Review

Lin, Xiang; Li, Jianhua; Wang, Shilin; Liew, Alan Wee‐Chung; Feng, Chengjian; Huang, Xiaofeng

doi:10.1016/j.eng.2018.02.008

Cited by 74 publications

(32 citation statements)

References 34 publications

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“…The images file is used as evidence and digital forensic based analysis [4,5,13]. The acquisition of image files called bitstream is implemented to maintain valid evidence according to digital forensic provisions [14]. The bitstream technique copies the original bit-by-bit files that are in the system file in the form of binary numbers.…”

Section: Results Of Internal Investigationsmentioning

confidence: 99%

Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application

2019

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Data is something that can be manipulated by irresponsible people and causing fraud. The use of log files, data theft, unauthorized person, and infiltration are things that should be prevented before the problem occurs. The authenticity of application data that evaluates the performance of company employees is very crucial. This paper will explain how to maintain the company’s big data as a valid standard service to assess employee performance database, based on employee performance of MariaDB or MySQL 5.0.11 with InnoDB storage engine. The authenticity of data is required for decent digital evidence when sabotage occurs. Digital forensic analysis carried out serves to reveal past activities, record time and be able to recover deleted data in the InnoDB storage engine table. A comprehensive examination is carried out by looking at the internal and external aspects of the Relational Database Management System (RDBMS). The result of this research is in the form of forensic tables in the InnoDB engine before and after sabotage occurs.

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Section: Results Of Internal Investigationsmentioning

confidence: 99%

Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application

2019

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“…Techniques such as double JPEG, JPEG blocks and JPEG quantization are exploited for detection in compressed images 4. Camera-based: used unique signature left by during the image acquisition and image storage in term of the lens, and sensor noises [10]. Some of the artifacts studied are color correlation, white balancing, quantization tables, filtering and JPEG compression.…”

Section: Various Passive Methods Are Implemented To Negate the Image mentioning

confidence: 99%

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

et al. 2019

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The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

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“…Here we provide more experiments on Ver 1 and Ver 2 subsets (summarized in Tables. 4,5,6,7,8,9). We test with more classifiers including Extra Trees classifiers (ET1, ET2), Nearest Neighbor classifier with different distance measures (L1,L2,Chebyshev), SVM on full feature and varying number of principal components (32,64,128,256), and a single layer dense neural network classifier trained for different lengths of time (exponential with respect to parameter shown in column header).…”

Section: Author Biographymentioning

confidence: 99%

Deep Learning Methods for Event Verification and Image Repurposing Detection

Goebel¹,

Flenner²,

Nataraj³

et al. 2019

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The authenticity of images posted on social media is an issue of growing concern. Many algorithms have been developed to detect manipulated images, but few have investigated the ability of deep neural network based approaches to verify the authenticity of image labels, such as event names. In this paper, we propose several novel methods to predict if an image was captured at one of several noteworthy events. We use a set of images from several recorded events such as storms, marathons, protests, and other large public gatherings. Two strategies of applying pre-trained Imagenet network for event verification are presented, with two modifications for each strategy. The first method uses the features from the last convolutional layer of a pre-trained network as input to a classifier. We also consider the effects of tuning the convolutional weights of the pre-trained network to improve classification. The second method combines many features extracted from smaller scales and uses the output of a pre-trained network as the input to a second classifier. For both methods, we investigated several different classifiers and tested many different pre-trained networks. Our experiments demonstrate both these approaches are effective for event verification and image re-purposing detection. The classification at the global scale tends to marginally outperform our tested local methods and fine tuning the network further improves the results.

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Recent Advances in Passive Digital Image Security Forensics: A Brief Review

Cited by 74 publications

References 34 publications

Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application

Digital Forensic InnoDB Database Engine for Employee Performance Appraisal Application

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

Deep Learning Methods for Event Verification and Image Repurposing Detection

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