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Keunsung Bae - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Quantization Step Size Against Amplitude Modification Attack in Scalar Quantization-Based Audio Watermarking
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:Scalar Quantization-based watermarking schemes are very vulnerable to amplitude modification attack. To overcome this problem, we propose a novel and robust algorithm that estimates the modified Quantization Step size by searching QE (Quantization error) function. For efficient searching of QE curve, we analyze the peak curve of QE analytically and derive the equations to determine an appropriate search interval. The search interval can be determined from the mean and variance of an audio signal regardless of its probability density function shape. The experimental results demonstrate that the proposed algorithm provides both exact estimation of the modified Quantization Step size and good watermark detection performance under AWGN attack
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Analysis of optimal search interval for estimation of modified Quantization Step size in Quantization-based audio watermark detection
Lecture Notes in Computer Science, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:The Quantization-based watermarking schemes such as QIM or SCS are known to be very vulnerable to the amplitude modification attack. The amplitude modification attack results in the change of Quantization Step size so the estimation of a modified Quantization Step size is required before watermark detection. In this paper, we analyze the Quantization error function of the audio signal having any shape of probability density function, and analytically determine the search interval that minimizes the Quantization error considering both detection performance and computational complexity. It is shown that the appropriate search interval can be determined from the frame-based mean and variance of the input signal without regard to its shape of probability density function. Experimental results for real audio data verify that the derived search interval provides the accurate estimation of the modified Quantization Step size under amplitude modification attack.
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IWDW - Analysis of optimal search interval for estimation of modified Quantization Step size in Quantization-based audio watermark detection
Digital Watermarking, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:The Quantization-based watermarking schemes such as QIM or SCS are known to be very vulnerable to the amplitude modification attack. The amplitude modification attack results in the change of Quantization Step size so the estimation of a modified Quantization Step size is required before watermark detection. In this paper, we analyze the Quantization error function of the audio signal having any shape of probability density function, and analytically determine the search interval that minimizes the Quantization error considering both detection performance and computational complexity. It is shown that the appropriate search interval can be determined from the frame-based mean and variance of the input signal without regard to its shape of probability density function. Experimental results for real audio data verify that the derived search interval provides the accurate estimation of the modified Quantization Step size under amplitude modification attack.
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IWDW - Robust estimation of amplitude modification for scalar costa scheme based audio watermark detection
Digital Watermarking, 2005Co-Authors: Siho Kim, Keunsung BaeAbstract:Recently, informed watermarking schemes based on Costa's dirty paper coding are drawing more attention than spread spectrum based techniques because these kinds of watermarking algorithms do not need an original host signal for watermark detection and the host signal does not affect the performance of watermark detection. For practical implementation, they mostly use uniform scalar quantizers, which are very vulnerable against amplitude modification. Hence, it is necessary to estimate the amplitude modification, i.e., a modified Quantization Step size, before watermark detection. In this paper, we propose a robust algorithm to estimate the modified Quantization Step size with an optimal search interval. It searches the Quantization Step size to minimize the Quantization error of the received audio signal. It does not encroach the space for embedding watermark message because it just uses the received signal itself for estimation of the Quantization Step size. The optimal searching interval is determined to satisfy both detection performance and computational complexity. Experimental results show that the proposed algorithm can estimate the modified Quantization Step size accurately under amplitude modification attacks.
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ICASSP (5) - Estimation of Quantization Step Size Against Amplitude Modification Attack in Scalar Quantization-Based Audio Watermarking
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1Co-Authors: Siho Kim, Keunsung BaeAbstract:Scalar Quantization-based watermarking schemes are very vulnerable to amplitude modification attack. To overcome this problem, we propose a novel and robust algorithm that estimates the modified Quantization Step size by searching QE (Quantization error) function. For efficient searching of QE curve, we analyze the peak curve of QE analytically and derive the equations to determine an appropriate search interval. The search interval can be determined from the mean and variance of an audio signal regardless of its probability density function shape. The experimental results demonstrate that the proposed algorithm provides both exact estimation of the modified Quantization Step size and good watermark detection performance under AWGN attack.
Siho Kim - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Quantization Step Size Against Amplitude Modification Attack in Scalar Quantization-Based Audio Watermarking
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:Scalar Quantization-based watermarking schemes are very vulnerable to amplitude modification attack. To overcome this problem, we propose a novel and robust algorithm that estimates the modified Quantization Step size by searching QE (Quantization error) function. For efficient searching of QE curve, we analyze the peak curve of QE analytically and derive the equations to determine an appropriate search interval. The search interval can be determined from the mean and variance of an audio signal regardless of its probability density function shape. The experimental results demonstrate that the proposed algorithm provides both exact estimation of the modified Quantization Step size and good watermark detection performance under AWGN attack
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Analysis of optimal search interval for estimation of modified Quantization Step size in Quantization-based audio watermark detection
Lecture Notes in Computer Science, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:The Quantization-based watermarking schemes such as QIM or SCS are known to be very vulnerable to the amplitude modification attack. The amplitude modification attack results in the change of Quantization Step size so the estimation of a modified Quantization Step size is required before watermark detection. In this paper, we analyze the Quantization error function of the audio signal having any shape of probability density function, and analytically determine the search interval that minimizes the Quantization error considering both detection performance and computational complexity. It is shown that the appropriate search interval can be determined from the frame-based mean and variance of the input signal without regard to its shape of probability density function. Experimental results for real audio data verify that the derived search interval provides the accurate estimation of the modified Quantization Step size under amplitude modification attack.
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IWDW - Analysis of optimal search interval for estimation of modified Quantization Step size in Quantization-based audio watermark detection
Digital Watermarking, 2006Co-Authors: Siho Kim, Keunsung BaeAbstract:The Quantization-based watermarking schemes such as QIM or SCS are known to be very vulnerable to the amplitude modification attack. The amplitude modification attack results in the change of Quantization Step size so the estimation of a modified Quantization Step size is required before watermark detection. In this paper, we analyze the Quantization error function of the audio signal having any shape of probability density function, and analytically determine the search interval that minimizes the Quantization error considering both detection performance and computational complexity. It is shown that the appropriate search interval can be determined from the frame-based mean and variance of the input signal without regard to its shape of probability density function. Experimental results for real audio data verify that the derived search interval provides the accurate estimation of the modified Quantization Step size under amplitude modification attack.
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IWDW - Robust estimation of amplitude modification for scalar costa scheme based audio watermark detection
Digital Watermarking, 2005Co-Authors: Siho Kim, Keunsung BaeAbstract:Recently, informed watermarking schemes based on Costa's dirty paper coding are drawing more attention than spread spectrum based techniques because these kinds of watermarking algorithms do not need an original host signal for watermark detection and the host signal does not affect the performance of watermark detection. For practical implementation, they mostly use uniform scalar quantizers, which are very vulnerable against amplitude modification. Hence, it is necessary to estimate the amplitude modification, i.e., a modified Quantization Step size, before watermark detection. In this paper, we propose a robust algorithm to estimate the modified Quantization Step size with an optimal search interval. It searches the Quantization Step size to minimize the Quantization error of the received audio signal. It does not encroach the space for embedding watermark message because it just uses the received signal itself for estimation of the Quantization Step size. The optimal searching interval is determined to satisfy both detection performance and computational complexity. Experimental results show that the proposed algorithm can estimate the modified Quantization Step size accurately under amplitude modification attacks.
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ICASSP (5) - Estimation of Quantization Step Size Against Amplitude Modification Attack in Scalar Quantization-Based Audio Watermarking
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1Co-Authors: Siho Kim, Keunsung BaeAbstract:Scalar Quantization-based watermarking schemes are very vulnerable to amplitude modification attack. To overcome this problem, we propose a novel and robust algorithm that estimates the modified Quantization Step size by searching QE (Quantization error) function. For efficient searching of QE curve, we analyze the peak curve of QE analytically and derive the equations to determine an appropriate search interval. The search interval can be determined from the mean and variance of an audio signal regardless of its probability density function shape. The experimental results demonstrate that the proposed algorithm provides both exact estimation of the modified Quantization Step size and good watermark detection performance under AWGN attack.
Rémi Cogranne - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Primary Quantization Steps in Double-Compressed JPEG Images Using a Statistical Model of Discrete Cosine Transform
IEEE Access, 2019Co-Authors: Thanh Thai, Rémi CogranneAbstract:Double compression of images occurs when one compresses twice, possibly with different quality factors, a digital image. Estimation of the first compression parameter of such a double compression is of a crucial interest for image forensics since it may help revealing, for instance, the software or the source camera. This paper proposes an accurate method for estimating the primary Quantization Steps in double-compressed JPEG images. This original methodology is based on an accurate statistical model of Discrete Cosine Transform (DCT) coefficients that has been proposed in our previous works. We also present a thorough analysis of the double compression properties, carefully taking into account carefully the effect of round-off noise. This analysis is used to derive an accurate range of possible value for Quantization of primary DCT coefficients with respect to the secondary Quantization Step. Using both the statistical model of quantized DCT coefficients and the range of possible values of first Quantization Step, a model of the twice quantized DCT coefficients is established. Eventually, it is proposed to estimate the primary Quantization value by finding, among a set of possible candidates, the one that best match the proposed statistical model in terms of minimal symmetrized Kullback-Leibler (KL) divergence. Numerical experiments on large databases of real images and comparisons with state-of-the-art approaches emphasize the relevance of the proposed method. INDEX TERMS Digital image forensics, Double JPEG compression, Quantization Step estimation, Statistical image model, DCT coefficient analysis.
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JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Thanh Thai, Rémi Cogranne, Florent Retraint, Thi-ngoc-canh DoanAbstract:The goal of the paper is to propose an accurate method for estimating Quantization Steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the Quantization effect and the statistics of Discrete Cosine Transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of Quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true Quantization Step. From the candidate set of the true Quantization Step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal Quantization Step candidate. The proposed method can also be exploited to estimate the secondary Quantization table in a double-JPEG compressed image stored in lossless format, and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.
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jpeg Quantization Step estimation and its applications to digital image forensics
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Thanh Hai Thai, Rémi Cogranne, Florent Retraint, Thi-ngoc-canh DoanAbstract:The goal of this paper is to propose an accurate method for estimating Quantization Steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the Quantization effect and the statistics of discrete cosine transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of Quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true Quantization Step. From the candidate set of the true Quantization Step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal Quantization Step candidate. The proposed method can also be exploited to estimate the secondary Quantization table in a double-JPEG compressed image stored in lossless format and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.
Zhao Yuanyuan - One of the best experts on this subject based on the ideXlab platform.
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improved Quantization watermarking with an adaptive Quantization Step size and hvs
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2005Co-Authors: Zhao YuanyuanAbstract:This paper proposes a new image-adaptive watermarking technique which utilizes a new combination of an adaptive Quantization Step size and a HVS(human visual system) model in the wavelet domain. Here we use Quantization Index Modulation(QIM) method with an adaptive Quantization Step size to realize the embedding scheme. The HVS masking is accomplished pixel by pixel by take into account the luminance and the frequency content of all the image subbands. The watermarking consists of a pseudorandom sequence which is adaptively embedded into the subbands. As usual, the watermark bits are detected by a minimum distance detector. Experimental results prove the effectiveness of the new algorithm.
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KES (1) - Improved Quantization watermarking with an adaptive Quantization Step size and HVS
Lecture Notes in Computer Science, 2005Co-Authors: Zhao YuanyuanAbstract:This paper proposes a new image-adaptive watermarking technique which utilizes a new combination of an adaptive Quantization Step size and a HVS(human visual system) model in the wavelet domain. Here we use Quantization Index Modulation(QIM) method with an adaptive Quantization Step size to realize the embedding scheme. The HVS masking is accomplished pixel by pixel by take into account the luminance and the frequency content of all the image subbands. The watermarking consists of a pseudorandom sequence which is adaptively embedded into the subbands. As usual, the watermark bits are detected by a minimum distance detector. Experimental results prove the effectiveness of the new algorithm.
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KES (1) - Improved Quantization watermarking with an adaptive Quantization Step size and HVS
Lecture Notes in Computer Science, 2005Co-Authors: Zhao YuanyuanAbstract:This paper proposes a new image-adaptive watermarking technique which utilizes a new combination of an adaptive Quantization Step size and a HVS(human visual system) model in the wavelet domain. Here we use Quantization Index Modulation(QIM) method with an adaptive Quantization Step size to realize the embedding scheme. The HVS masking is accomplished pixel by pixel by take into account the luminance and the frequency content of all the image subbands. The watermarking consists of a pseudorandom sequence which is adaptively embedded into the subbands. As usual, the watermark bits are detected by a minimum distance detector. Experimental results prove the effectiveness of the new algorithm.
Thi-ngoc-canh Doan - One of the best experts on this subject based on the ideXlab platform.
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JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Thanh Thai, Rémi Cogranne, Florent Retraint, Thi-ngoc-canh DoanAbstract:The goal of the paper is to propose an accurate method for estimating Quantization Steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the Quantization effect and the statistics of Discrete Cosine Transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of Quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true Quantization Step. From the candidate set of the true Quantization Step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal Quantization Step candidate. The proposed method can also be exploited to estimate the secondary Quantization table in a double-JPEG compressed image stored in lossless format, and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.
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jpeg Quantization Step estimation and its applications to digital image forensics
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Thanh Hai Thai, Rémi Cogranne, Florent Retraint, Thi-ngoc-canh DoanAbstract:The goal of this paper is to propose an accurate method for estimating Quantization Steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the Quantization effect and the statistics of discrete cosine transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of Quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true Quantization Step. From the candidate set of the true Quantization Step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal Quantization Step candidate. The proposed method can also be exploited to estimate the secondary Quantization table in a double-JPEG compressed image stored in lossless format and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.
