The Experts below are selected from a list of 10923 Experts worldwide ranked by ideXlab platform
Yunqing Shi - One of the best experts on this subject based on the ideXlab platform.
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detecting double jpeg compressed color images with the same Quantization Matrix in spherical coordinates
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Jinwei Wang, Hao Wang, Xiangyang Luo, Yunqing Shi, Sunil Kr JhaAbstract:Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different Quantization Matrix, the detection of double JPEG compression with the same Quantization Matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and Quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
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an effective method for detecting double jpeg compression with the same Quantization Matrix
IEEE Transactions on Information Forensics and Security, 2014Co-Authors: Jianquan Yang, Jin Xie, Guopu Zhu, Sam Kwong, Yunqing ShiAbstract:Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different Quantization matrices. However, detecting double JPEG compression with the same Quantization Matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the state-of-the-art method.
Yao Zhao - One of the best experts on this subject based on the ideXlab platform.
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detection of double jpeg compression with the same Quantization Matrix via convergence analysis
IEEE Transactions on Circuits and Systems for Video Technology, 2021Co-Authors: Yakun Niu, Yao ZhaoAbstract:Detecting double JPEG compression with the same Quantization Matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward Quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.
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primary Quantization Matrix estimation of double compressed jpeg images via cnn
arXiv: Image and Video Processing, 2019Co-Authors: Yakun Niu, Yao Zhao, Benedetta Tondi, Mauro BarniAbstract:Available model-based techniques for the estimation of the primary Quantization Matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a very general range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and accuracy especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches.
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an enhanced approach for detecting double jpeg compression with the same Quantization Matrix
Signal Processing-image Communication, 2019Co-Authors: Yakun Niu, Yao ZhaoAbstract:Abstract The number of different JPEG coefficients between two successive JPEG compressed images with the same Quantization Matrix will monotonically decrease in general. This intrinsic property can be used to detect double JPEG compression with the same Quantization Matrix. However, such a decreasing tendency is not significant for some JPEG images compressed with low quality factors (QFs). It is especially difficult to detect double JPEG compression in this scenario. By analyzing the statistical properties of JPEG coefficients with various QFs, an enhanced random perturbation based double JPEG compression detection method is proposed in this paper. Unlike existing random perturbation strategy that just indiscriminately selects JPEG coefficients for modification, in the proposed method, only JPEG coefficients with values ± 1 are modified by a novel modification strategy. Experimental results demonstrate that the proposed method outperforms some state-of-the-art detection methods, including the original random perturbation based one, especially for the case of low QFs.
Yakun Niu - One of the best experts on this subject based on the ideXlab platform.
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detection of double jpeg compression with the same Quantization Matrix via convergence analysis
IEEE Transactions on Circuits and Systems for Video Technology, 2021Co-Authors: Yakun Niu, Yao ZhaoAbstract:Detecting double JPEG compression with the same Quantization Matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward Quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.
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primary Quantization Matrix estimation of double compressed jpeg images via cnn
arXiv: Image and Video Processing, 2019Co-Authors: Yakun Niu, Yao Zhao, Benedetta Tondi, Mauro BarniAbstract:Available model-based techniques for the estimation of the primary Quantization Matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a very general range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and accuracy especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches.
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an enhanced approach for detecting double jpeg compression with the same Quantization Matrix
Signal Processing-image Communication, 2019Co-Authors: Yakun Niu, Yao ZhaoAbstract:Abstract The number of different JPEG coefficients between two successive JPEG compressed images with the same Quantization Matrix will monotonically decrease in general. This intrinsic property can be used to detect double JPEG compression with the same Quantization Matrix. However, such a decreasing tendency is not significant for some JPEG images compressed with low quality factors (QFs). It is especially difficult to detect double JPEG compression in this scenario. By analyzing the statistical properties of JPEG coefficients with various QFs, an enhanced random perturbation based double JPEG compression detection method is proposed in this paper. Unlike existing random perturbation strategy that just indiscriminately selects JPEG coefficients for modification, in the proposed method, only JPEG coefficients with values ± 1 are modified by a novel modification strategy. Experimental results demonstrate that the proposed method outperforms some state-of-the-art detection methods, including the original random perturbation based one, especially for the case of low QFs.
Sunil Kr Jha - One of the best experts on this subject based on the ideXlab platform.
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detecting double jpeg compressed color images with the same Quantization Matrix in spherical coordinates
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Jinwei Wang, Hao Wang, Xiangyang Luo, Yunqing Shi, Sunil Kr JhaAbstract:Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different Quantization Matrix, the detection of double JPEG compression with the same Quantization Matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and Quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
Yun Q. Shi - One of the best experts on this subject based on the ideXlab platform.
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detection of double jpeg compression with the same Quantization Matrix based on convolutional neural networks
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2018Co-Authors: Peng Peng, Tanfeng Sun, Xinghao Jiang, Yun Q. ShiAbstract:The detection of double JPEG compression with the same Quantization Matrix is a challenging problem in image forensics. In this paper, a CNN framework is proposed to solve this problem. This framework contains a preprocessing layer and a well-designed CNN. In the preprocessing layer, the rounding and truncation error images are extracted from continuous recompressed input samples and then fed into the following CNN. In the design of the CNN architecture, several advanced techniques are carefully considered to prevent overfitting, such as $1\times 1$ convolutional kernel and global average pooling layer. The performance of proposed framework is evaluated on the public available image dataset (BOSSbase) with various quality factors (QF). Experimental results have shown the proposed CNN framework performs better than the state-of-the-art method based on hand-crafted features.
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Detecting Double JPEG Compression With the Same Quantization Matrix
IEEE Transactions on Information Forensics and Security, 2010Co-Authors: Fangjun Huang, Jiwu Huang, Yun Q. ShiAbstract:Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different Quantization Matrixes. However, when the primary compression and the secondary compression have the same Quantization Matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same Quantization Matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same Quantization Matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.
