The Experts below are selected from a list of 32346 Experts worldwide ranked by ideXlab platform
K.j. Ray Liu - One of the best experts on this subject based on the ideXlab platform.
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Robust Median Filtering forensics using an autoregressive model
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is Median Filtering. While several Median Filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized Filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust Median Filtering forensic technique. It operates by analyzing the statistical properties of the Median filter residual (MFR), which we define as the difference between an image in question and a Median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for Median filter detection. We test the effectiveness of our proposed Median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.
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Anti-forensics of Median Filtering
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2013Co-Authors: Zhung Han Wu, Matthew C. Stamm, K.j. Ray LiuAbstract:A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is Median Filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several Median Filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of Median Filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a Median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains Median Filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing Median Filtering detectors under realistic conditions.
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ICASSP - Anti-forensics of Median Filtering
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Matthew C. Stamm, K.j. Ray LiuAbstract:A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is Median Filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several Median Filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of Median Filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a Median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains Median Filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing Median Filtering detectors under realistic conditions.
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APSIPA - Robust Median Filtering forensics based on the autoregressive model of Median filtered residual
2012Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:One important aspect of multimedia forensics is exposing an image's processing history. Median Filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of Median Filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust Median Filtering detection scheme based on the autoregressive model of Median filtered residual. Median Filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the Median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window Filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed Median Filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between Median Filtering and other manipulations, such as Gaussian Filtering, average Filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
Mauro Barni - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Hiding traces of Median Filtering in digital images
2012Co-Authors: Marco Fontani, Mauro BarniAbstract:Detection of Median Filtering is an important task in image forensics, since this operator is frequently used both for benign and malicious processing. In this paper we introduce a counter-forensic technique that allows to conceal traces left by Median Filtering while preserving the quality of the processed image. The work aims to hide traces searched by state-of-the-art tools, and does not require JPEG compression of the image to hide traces.
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hiding traces of Median Filtering in digital images
European Signal Processing Conference, 2012Co-Authors: Marco Fontani, Mauro BarniAbstract:Detection of Median Filtering is an important task in image forensics, since this operator is frequently used both for benign and malicious processing. In this paper we introduce a counter-forensic technique that allows to conceal traces left by Median Filtering while preserving the quality of the processed image. The work aims to hide traces searched by state-of-the-art tools, and does not require JPEG compression of the image to hide traces.
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A quasi-Euclidean norm to speed up vector Median Filtering
IEEE Transactions on Image Processing, 2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, Valter CappelliniAbstract:For reducing impulsive noise without degrading image contours, Median Filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector Median filter can be used. Vector Median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector Median Filtering.
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ICIP - Optimum linear approximation of the Euclidean norm to speed up vector Median Filtering
IEEE Transactions on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, Valter CappelliniAbstract:For reducing impulsive noise without degrading image contours, Median Filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector Median filter can be used. Vector Median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean norm is presented which is applied to vector Median Filtering.
Valter Cappellini - One of the best experts on this subject based on the ideXlab platform.
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A quasi-Euclidean norm to speed up vector Median Filtering
IEEE Transactions on Image Processing, 2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, Valter CappelliniAbstract:For reducing impulsive noise without degrading image contours, Median Filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector Median filter can be used. Vector Median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector Median Filtering.
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ICIP - Optimum linear approximation of the Euclidean norm to speed up vector Median Filtering
IEEE Transactions on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, Valter CappelliniAbstract:For reducing impulsive noise without degrading image contours, Median Filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector Median filter can be used. Vector Median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean norm is presented which is applied to vector Median Filtering.
Xiangui Kang - One of the best experts on this subject based on the ideXlab platform.
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Median Filtering forensics based on convolutional neural networks
IEEE Signal Processing Letters, 2015Co-Authors: Jiansheng Chen, Xiangui Kang, Ye Liu, Jane Z WangAbstract:Median Filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image Median Filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting Median Filtering from small-size and compressed image blocks, by taking into account of the properties of Median Filtering, we propose a Median Filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying CNNs in Median Filtering image forensics. Unlike conventional CNN models, the first layer of our CNN framework is a filter layer that accepts an image as the input and outputs its Median Filtering residual (MFR). Then, via alternating convolutional layers and pooling layers to learn hierarchical representations, we obtain multiple features for further classification. We test the proposed method on several experiments. The results show that the proposed method achieves significant performance improvements, especially in the cut-and-paste forgery detection.
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countering anti forensics of Median Filtering
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li LiuAbstract:The statistical fingerprints left by Median Filtering can be a valuable clue for image forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove Median Filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-forensics of Median Filtering.
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Robust Median Filtering forensics using an autoregressive model
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is Median Filtering. While several Median Filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized Filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust Median Filtering forensic technique. It operates by analyzing the statistical properties of the Median filter residual (MFR), which we define as the difference between an image in question and a Median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for Median filter detection. We test the effectiveness of our proposed Median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.
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APSIPA - Robust Median Filtering forensics based on the autoregressive model of Median filtered residual
2012Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:One important aspect of multimedia forensics is exposing an image's processing history. Median Filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of Median Filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust Median Filtering detection scheme based on the autoregressive model of Median filtered residual. Median Filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the Median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window Filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed Median Filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between Median Filtering and other manipulations, such as Gaussian Filtering, average Filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
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Robust Median Filtering forensics based on the autoregressive model of Median filtered residual
Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, 2012Co-Authors: Xiangui Kang, Matthew C. Stamm, Anjie PengAbstract:One important aspect of multimedia forensics is exposing an image's processing history. Median Filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of Median Filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust Median Filtering detection scheme based on the autoregressive model of Median filtered residual. Median Filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the Median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window Filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed Median Filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between Median Filtering and other manipulations, such as Gaussian Filtering, average Filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
Matthew C. Stamm - One of the best experts on this subject based on the ideXlab platform.
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Robust Median Filtering forensics using an autoregressive model
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is Median Filtering. While several Median Filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized Filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust Median Filtering forensic technique. It operates by analyzing the statistical properties of the Median filter residual (MFR), which we define as the difference between an image in question and a Median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for Median filter detection. We test the effectiveness of our proposed Median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.
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Anti-forensics of Median Filtering
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2013Co-Authors: Zhung Han Wu, Matthew C. Stamm, K.j. Ray LiuAbstract:A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is Median Filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several Median Filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of Median Filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a Median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains Median Filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing Median Filtering detectors under realistic conditions.
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ICASSP - Anti-forensics of Median Filtering
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Matthew C. Stamm, K.j. Ray LiuAbstract:A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is Median Filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several Median Filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of Median Filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a Median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains Median Filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing Median Filtering detectors under realistic conditions.
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APSIPA - Robust Median Filtering forensics based on the autoregressive model of Median filtered residual
2012Co-Authors: Xiangui Kang, Anjie Peng, Matthew C. Stamm, K.j. Ray LiuAbstract:One important aspect of multimedia forensics is exposing an image's processing history. Median Filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of Median Filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust Median Filtering detection scheme based on the autoregressive model of Median filtered residual. Median Filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the Median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window Filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed Median Filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between Median Filtering and other manipulations, such as Gaussian Filtering, average Filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
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Robust Median Filtering forensics based on the autoregressive model of Median filtered residual
Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, 2012Co-Authors: Xiangui Kang, Matthew C. Stamm, Anjie PengAbstract:One important aspect of multimedia forensics is exposing an image's processing history. Median Filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of Median Filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust Median Filtering detection scheme based on the autoregressive model of Median filtered residual. Median Filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the Median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window Filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed Median Filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between Median Filtering and other manipulations, such as Gaussian Filtering, average Filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
