A Survey on Digital Camera Image Forensic Methods

Lanh, Tran Van; Chong, Kai-Sen; Emmanuel, Sabu; Kankanhalli, Mohan

doi:10.1109/icme.2007.4284575

Cited by 93 publications

(41 citation statements)

References 17 publications

(28 reference statements)

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“…Confirming the authenticity of the digital image based on criteria like integrity and source is the actual field of concern of DIF. Distinguishing proof of source includes deciding the methods by which the images are taken such as camera, scanner, and regenerative means [4,5]. Likewise the, integrity of the image can be affirmed by examining the image for its alterations basically called as tamper detection.…”

Section: Need For Difmentioning

confidence: 99%

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Medikonda¹,

Janarthanan²

2017

DJ JAECE

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As images are increasingly being used today as evidences for criminal justification, the forgery on the images that are used for criminal investigations can cause a great threat in the implementation of truth and justice. The innovations in digitizing technology along with image editing software have made it very easy to tamper digital images. So, there is a need for examining the authenticity of the images. Digital image forensics has emerged to solve the problems behind this conflicting issue. In this work, the test images are pre-processed by means of blur-index calculation and false colour removal with the help of nearest neighbour algorithm. Next the colour feature extraction process is carried out. This is done by generating the histogram of the image, and feeding it as input to the Colour Index Local Auto-Correlations (CILAC) model, to extract the colour index. This facilitates the enhanced Auto Colour Correlation (ACC) approach. Next the image undergoes 8Z affine transformation and the images are matched to obtain the result. Finally, a comparison of the results obtained with the other existing methods is done which reveals that the ACC enhanced approach has a better performance in terms of precision, recall and F1 score.

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Section: Need For Difmentioning

confidence: 99%

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Medikonda¹,

Janarthanan²

2017

DJ JAECE

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“…The biggest problem with this approach is that different models of digital cameras use components of a small number of manufacturers, and the algorithms used are also very similar between models of the same brand. According to [4] [5] proposes a method to identify the source using the following features: color features, image quality metrics and frequency domain. The study adopted the wavelet transforms as a method to calculate the wavelet domain statistics and use a Support Vector Machine (SVM) for classification.…”

Section: Related Workmentioning

confidence: 99%

New Technique of Forensic Analysis for Digital Cameras in Mobile Devices

Corripio

Lucila²,

Orozco

et al. 2015

The 7th International Conference on Information Technology

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Abstract-Nowadays, forensic analysis of digital images is especially important, given the high use of digital cameras in mobile devices. The identification of the device type or the make and model of image source are two important branches of forensic analysis of digital images. In this paper we have addressed both, with an approach based on different types of image features and the classification using support vector machines. The study mainly has focused on images created with mobile devices and as a result, the techniques and features have been adapted or created for this purpose. There have been a total of 36 experiments classified into 5 sets, in order to test different configurations of the techniques. In the configuration of the experiments were taken into account among other things the future use of the technique by the forensic analyst in real situations and creating experiments with high technical requirements.

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“…Hence, for conventional and online media outlets, the capability to detect doctored images before they are published is important to maintain credibility. Recent research efforts in the field of media forensics have begun to address these issues [3], [4], [5].…”

mentioning

confidence: 99%

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

Dirik¹,

Sencar

Memon

2008

IEEE Trans.Inform.Forensic Secur.

150

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Abstract-Digital single lens reflex cameras suffer from a well known sensor dust problem due to interchangeable lenses they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex (DSLR) camera at low false positive rates, even under heavy compression and downsampling.

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A Survey on Digital Camera Image Forensic Methods

Cited by 93 publications

References 17 publications

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

New Technique of Forensic Analysis for Digital Cameras in Mobile Devices

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

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