Robust Median Filtering Forensics Using an Autoregressive Model

Kang, Xiangui; Stamm, Matthew C.; Peng, Anjie; Liu, K. J. Ray

doi:10.1109/tifs.2013.2273394

Cited by 218 publications

(188 citation statements)

References 23 publications

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“…According to this fact, most state-of-the-art MF detectors [2][3][4][5] for the feature vector used different lengths and the employed some methods for the extraction of the feature set. To extend the length of the feature vector to increase the classification ratio between the altered and unaltered images, the extraction time of the feature set and the training-testing time both take longer.…”

Section: Feature Set For Median Filtering Detectionmentioning

confidence: 99%

“…Furthermore, MF detection could classify the altered images with MF. 1 Consequently, the prior studies [2][3][4][5] emphasized that the MF detector becomes a significant forensic tool for the recovery of the processing history of a forgery image.…”

Section: Introductionmentioning

confidence: 99%

“…To extract the 10 features for MF detection (MFD), Kang et al 2 obtained autoregressive (AR) coefficients as feature vectors via an AR model to analyze the median filter residual (MFR AR), which is the difference between the values of the original image and those of the median-filtered image. The authors analyzed an image's MFR AR; it is able to suppress image content that may interfere with MFD.…”

Section: Introductionmentioning

confidence: 99%

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Median filtering detection using variation of neighboring line pairs for image forensics

Rhee

2016

J. Electron. Imaging

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Abstract. Attention to tampering by median filtering (MF) has recently increased in digital image forensics. For the MF detection (MFD), this paper presents a feature vector that is extracted from two kinds of variations between the neighboring line pairs: the row and column directions. Of these variations in the proposed method, one is defined by a gradient difference of the intensity values between the neighboring line pairs, and the other is defined by a coefficient difference of the Fourier transform (FT) between the neighboring line pairs. Subsequently, the constructed 19-dimensional feature vector is composed of these two parts. One is the extracted 9-dimensional from the space domain of an image and the other is the 10-dimensional from the frequency domain of an image. The feature vector is trained in a support vector machine classifier for MFD in the altered images. As a result, in the measured performances of the experimental items, the area under the receiver operating characteristic curve (AUC, ROC) by the sensitivity (P TP : the true positive rate) and 1-specificity (P FP : the false-positive rate) are above 0.985 and the classification ratios are also above 0.979. P e (a minimal average decision error) ranges from 0 to 0.024, and P TP at P FP ¼ 0.01 ranges from 0.965 to 0.996. It is confirmed that the grade evaluation of the proposed variation-based MF detection method is rated as "Excellent (A)" by AUC is above 0.9. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Feature Set For Median Filtering Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Median filtering detection using variation of neighboring line pairs for image forensics

Rhee

2016

J. Electron. Imaging

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“…(MF 3 ), 5×5 (MF 5 ) and 7×7 (MF 7 ) respectively in each MF w , w∈{3, 5, 7}, and it has different eight level…”

Section: ⅰ 서 론mentioning

confidence: 99%

“…[5] The median in statistic means the value in the middle of the items or literally halfway down the list of items ⎾w/2⏋.…”

Section: ⅱ Theoretical Background Of Mf Operationmentioning

confidence: 99%

Median Filtering Detection using Latent Growth Modeling

Rhee¹

2015

Journal of the Institute of Electronics and Information Enginee

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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.

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Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

Song,

Lin,

Xie

2024

Advanced Intelligent Systems

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This article introduces the synergistic dual‐stream network (SDS‐Net), a novel neural network architecture that significantly enhances the detection of image manipulations. SDS‐Net employs a unique dual‐stream fusion strategy that processes both RGB image and the corresponding noise map. It innovatively combines features computation from the different blocks of dual backbones, and leverages a multi‐scale spatial pyramid pooling (MSPP) module to expand the receptive fields of shallow features. This approach not only enriches the feature representation, but also ensures the precise localization of manipulated regions. Extensive experiments conducted on various public datasets demonstrate the superiority of SDS‐Net over several state‐of‐the‐art methods.

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Robust Median Filtering Forensics Using an Autoregressive Model

Cited by 218 publications

References 23 publications

Median filtering detection using variation of neighboring line pairs for image forensics

Median filtering detection using variation of neighboring line pairs for image forensics

Median Filtering Detection using Latent Growth Modeling

Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

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