Image splicing detection using 2-D phase congruency and statistical moments of characteristic function

Wen, Chenglu; Shi, Yun Q.; Su, Wei

doi:10.1117/12.704321

Cited by 120 publications

(66 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, numerous methods for detecting digital forgeries were proposed [15][16][17][18][19][20][21][22][23][24][25][26][27]38]. Most methods are based on detecting local inconsistencies, such as in resampling artifacts [19], color filter array (CFA) interpolation artifacts [20], illumination [21], or optical defects [23].…”

Section: Introductionmentioning

confidence: 99%

Determining Image Origin and Integrity Using Sensor Noise

Chen¹,

Fridrich²,

Goljan

et al. 2008

IEEE Trans.Inform.Forensic Secur.

782

688

View full text Add to dashboard Cite

Abstract-In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a Maximum Likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

show abstract

Section: Introductionmentioning

confidence: 99%

Determining Image Origin and Integrity Using Sensor Noise

Chen¹,

Fridrich²,

Goljan

et al. 2008

IEEE Trans.Inform.Forensic Secur.

782

688

View full text Add to dashboard Cite

show abstract

“…Alternative approaches to detection of digital forgeries were described by other researchers in [33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Forgery Detectionmentioning

confidence: 99%

Advanced Digital Forensic and Steganalysis Methods

Fridrich¹

2009

View full text Add to dashboard Cite

REPORT DOCUMENTATION PAGEThe public reporting burden for th^^Tllection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)The Research All data delivered is approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe author developed and advanced a new class of digital forensic techniques to verify origin and integrity of digital imagery based on systematic artifacts of imaging sensors called photo-response non-uniformity (PRNU), which is caused by slight variations in physical dimensions of pixels and inhomogeneity of silicon. PRNU thus forms a unique fingerprint that characterizes an imaging device, such as a camera, camcorder, or scanner. Both CCD and CMOS technologies exhibit this type of irregularity. The specific achievements include perfecting the methodology for estimating the fingerprint from images, extending to cases when the image under investigation is simultaneously cropped, scaled, and processed, extending the technology when the digital image is printed, developing technology capable of determining the camera model from the fingerprint, and a large scale validation on millions of images from 6900 cameras of 150 models. The techniques have dual purpose and are important for information validation in military intelligence gathering, law enforcement, and forensic investigation. SUBJECT TERMSDigital Forensics, sensor, fingerprint, identification, forgery detection, authentication, information assurance, media security

show abstract

“…Bicoherence is a normalized bispectrum, i.e., the third order correlation of three harmonically related Fourier frequencies of a signal, which appears to capture quite well the discontinuities introduced in the image after splicing. Methods in [17,35,84] propose alternative techniques based on the Hilbert-Huang transform, on statistics of 2-D phase congruency and on wavelet sub-bands features together with Markov transition probabilities of difference JPEG 2-D arrays, respectively. The latter, in particular, outperforms the other techniques when applied on the Columbia Image Splicing Detection Evaluation Dataset [71].…”

Section: Detecting Image Compositionmentioning

confidence: 99%

Digital image forensics: a booklet for beginners

Redi

Taktak

Dugelay

2010

Multimed Tools Appl

300

112

View full text Add to dashboard Cite

Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

show abstract

Image splicing detection using 2-D phase congruency and statistical moments of characteristic function

Cited by 120 publications

References 12 publications

Determining Image Origin and Integrity Using Sensor Noise

Determining Image Origin and Integrity Using Sensor Noise

Advanced Digital Forensic and Steganalysis Methods

Digital image forensics: a booklet for beginners

Contact Info

Product

Resources

About