Forensic detection of median filtering in digital images

Cao, Gang; Zhao, Yao; Ni, Rongrong; Yu, Lifang; Tian, Huawei

doi:10.1109/icme.2010.5583869

Cited by 126 publications

(84 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1] Focused on the blind detection of median filtering rely on the probability of zero values on the first order difference map in texture regions. Gradient aberration of the gray histogram and ringing artifacts around step edges were measured and employed to capture trails of sharpening operation in [6].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, image enhancement alterations fall into many categories [1], [6] and the detection history involving contrast enhancement is not long. M.C.Stamm and K. J. R. Liu [9], [11], [12] have constructed a model for the histogram of an unaltered image, and took advantage of this model to detect artifacts left behind by alterations in the form of global and local contrast enhancement operations, obtaining d P 0.99 based on the G channel in the RGB color model.…”

Section: Related Workmentioning

confidence: 99%

“…enhancement globally or locally to the tampered image will enable a more natural mosaic image not only to retouch or conceal visual evidence of forgery, but also in an attempt to destroy forensically significant indicators of these tampering operations. Contrast enhancement belongs to the category of content-preserving manipulations [1] as well as resampling [2], compression [3], blur [4], [5] and sharpening [6] other than content-changing tampering such as splicing and copy-move [7], [8]. In brief, strengthening contrast enhancement detection is of great significance since the type of operations is also frequently used to merely change image perceptual quality in the general image processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Tampered Image Detecting Method Based on Wavelet Analysis

Bu¹

2012

IJIEE

View full text Add to dashboard Cite

Abstract-Contrast enhancement operation is often used to highlight some information, weaken or remove some unwanted information in tampered images. In this paper, a tampered image detecting method is proposed based on wavelet analysis for specific fingerprints left by contrast enhancement operation in image. Firstly, the RGB color space of image was converted into YCbCr color space, and the Y monochrome channel image was extracted; secondly, the normalized energy was calculated in the wavelet details sub-bands after wavelet transform of image's pixel value histogram of the component; finally, the tampered image was identified according to the normalized energy. The results of the experiment show there is an obvious distinction between tampered and unaltered sub-blocks divided. Furthermore, the contrast experiment results about wavelet transform and Fourier transform show that the former is better than the latter in both accuracy and time complexity.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Tampered Image Detecting Method Based on Wavelet Analysis

Bu¹

2012

IJIEE

View full text Add to dashboard Cite

show abstract

“…To detect MF in forgery images, G. Cao et al [6] analyzed the probability that an image's first-order pixel difference is zero in textured regions. In this regard, Ray Liu [9] account for the scheme which is highly accurate in unaltered or uncompressed images.…”

Section: ⅰ 서 론mentioning

confidence: 99%

“…The preceding step is to compute the model specifications of SEM Sk (refer in Fig. 7 and    are computed by (6) and (7) If T is median filtered, according to k, median filtering window size is classified by (8).…”

Section: Proposed Algorithmmentioning

confidence: 99%

Median Filtering Detection using Latent Growth Modeling

Rhee¹

2015

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

In recent times, the median filtering (MF) detector as a forensic tool for the recovery of forgery images' processing history has concerned broad interest. For the classification of MF image, MF detector should be designed with smaller feature set and higher detection ratio. This paper presents a novel method for the detection of MF in altered images. It is transformed from BMP to several kinds of MF image by the median window size. The difference distribution values are computed according to the window sizes and then the values construct the feature set same as the MF window size. For the MF detector, the feature set transformed to the model specification which is computed using latent growth modeling (LGM). Through experiments, the test image is classified by the discriminant into two classes: the true positive (TP) and the false negative (FN). It confirms that the proposed algorithm is to be outstanding performance when the minimum distance average is 0.119 in the confusion of TP and FN for the effectivity of classification.

show abstract

Counter-Forensics: Attacking Image Forensics

Böhme

Kirchner

2012

Digital Image Forensics

115

View full text Add to dashboard Cite

This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter.

show abstract

Forensic detection of median filtering in digital images

Cited by 126 publications

References 11 publications

A Tampered Image Detecting Method Based on Wavelet Analysis

A Tampered Image Detecting Method Based on Wavelet Analysis

Median Filtering Detection using Latent Growth Modeling

Counter-Forensics: Attacking Image Forensics

Contact Info

Product

Resources

About