Image forensics of digital cameras by analysing image variations using Statistical Process Control

Bateman, Philip; Ho, Anthony T. S.; Woodward, Alan

doi:10.1109/icics.2009.5397649

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Another approach which employed Statistical Process Control (SPC) charts on image variations has been introduced by Bateman in [11]. In their paper, the charts act as a tool to detect anomalies in image data.…”

Section: Camera Identificationmentioning

confidence: 99%

Advanced video camera identification using conditional probability features

Yahaya¹,

Ho²,

Wahab³

2012

IET Conference on Image Processing (IPR 2012)

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Today, the misuse of digital data especially images and videos become crucial with the existence of sophisticated high-tech equipment and it is available at relatively low cost. Illegal recording of movie in cinema has caused losses of millions of dollars a year. Law enforcement agencies are keen to find ways to counter illegal video recording. Current research into camera identification techniques is attracting a significant amount of attention. The main objective is to identify the camera equipment used to record digital image or video based on the data source obtained. In this paper, we propose a video camera identification technique based on the Conditional Probability (CP) Features. Specifically we focus on its performance for identification of video sources using cameras of different models. In our experiments, we demonstrate that the CP Features are able to correctly match the test video frames with their source with classification accuracy is approximately 97.2%. These findings provide a good indication that CP Features are suitable for digital video forensics.

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Section: Camera Identificationmentioning

confidence: 99%

Advanced video camera identification using conditional probability features

Yahaya¹,

Ho²,

Wahab³

2012

IET Conference on Image Processing (IPR 2012)

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“…Fridrich et al [2] classified image forensic techniques according to six different categories: source classification; device identification; images linking to source device [4]; processing history recovery; integrity detection; and anomaly investigation. Processing history recovery relates to the part recovery of the processing chain associated with an image [2].…”

Section: Introductionmentioning

confidence: 99%

Estimating JPEG2000 compression for image forensics using Benford's Law

Qādir

Zhao

2010

SPIE Proceedings

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With the tremendous growth and usage of digital images nowadays, the integrity and authenticity of digital content is becoming increasingly important, and a growing concern to many government and commercial sectors. Image Forensics, based on a passive statistical analysis of the image data only, is an alternative approach to the active embedding of data associated with Digital Watermarking.Benford's Law was first introduced to analyse the probability distribution of the 1 st digit (1-9) numbers of natural data, and has since been applied to Accounting Forensics for detecting fraudulent income tax returns [9]. More recently, Benford's Law has been further applied to image processing and image forensics. For example, Fu et al. [5] proposed a Generalised Benford's Law technique for estimating the Quality Factor (QF) of JPEG compressed images. In our previous work, we proposed a framework incorporating the Generalised Benford's Law to accurately detect unknown JPEG compression rates of watermarked images in semi-fragile watermarking schemes. JPEG2000 (a relatively new image compression standard) offers higher compression rates and better image quality as compared to JPEG compression. In this paper, we propose the novel use of Benford's Law for estimating JPEG2000 compression for image forensics applications. By analysing the DWT coefficients and JPEG2000 compression on 1338 test images, the initial results indicate that the 1 st digit probability of DWT coefficients follow the Benford's Law. The unknown JPEG2000 compression rates of the image can also be derived, and proved with the help of a divergence factor, which shows the deviation between the probabilities and Benford's Law.Based on 1338 test images, the mean divergence for DWT coefficients is approximately 0.0016, which is lower than DCT coefficients at 0.0034. However, the mean divergence for JPEG2000 images compression rate at 0.1 is 0.0108, which is much higher than uncompressed DWT coefficients. This result clearly indicates a presence of compression in the image. Moreover, we compare the results of 1 st digit probability and divergence among JPEG2000 compression rates at 0.1, 0.3, 0.5 and 0.9. The initial results show that the expected difference among them could be used for further analysis to estimate the unknown JPEG2000 compression rates.

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Copy-paste forgery detection using deep learning with error level analysis

Sasikala

Shanmuganathan

2023

Multimed Tools Appl

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Image forensics of digital cameras by analysing image variations using Statistical Process Control

Cited by 4 publications

References 6 publications

Advanced video camera identification using conditional probability features

Advanced video camera identification using conditional probability features

Estimating JPEG2000 compression for image forensics using Benford's Law

Copy-paste forgery detection using deep learning with error level analysis

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