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Titlebook: Digital Forensics and Watermarking; 17th International W Chang D. Yoo,Yun-Qing Shi,Gwangsu Kim Conference proceedings 2019 Springer Nature

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書目名稱Digital Forensics and Watermarking
副標題17th International W
編輯Chang D. Yoo,Yun-Qing Shi,Gwangsu Kim
視頻videohttp://file.papertrans.cn/280/279333/279333.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Digital Forensics and Watermarking; 17th International W Chang D. Yoo,Yun-Qing Shi,Gwangsu Kim Conference proceedings 2019 Springer Nature
描述This book constitutes the refereed proceedings of the 17th International Workshop?.on Digital Forensics and Watermarking, IWDW 2018, held on Jeju Island, Korea, in October 2018..The 25 papers presented in this volume were carefully reviewed and selected from 43?submissions. The contributions are covering the following topics: deep neural networks for digital forensics; steganalysis and identification; watermarking; reversible data hiding; steganographic algorithms; identification and security;?deep generative models for forgery and its detection.?.
出版日期Conference proceedings 2019
關(guān)鍵詞cryptography; digital forensics; watermarking; steganalysis; steganography; security service; data hiding;
版次1
doihttps://doi.org/10.1007/978-3-030-11389-6
isbn_softcover978-3-030-11388-9
isbn_ebook978-3-030-11389-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Comparison of DCT and Gabor Filters in Residual Extraction of CNN Based JPEG Steganalysis filter and DCT filter are both used for residual extraction. However, there are few comparisons in existing convolutional neural networks (CNNs) based JPEG steganalysis using Gabor filter or DCT filter in the pre-processing stage to extract residuals. In this paper, we compare the performance of DC
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A Deep Residual Multi-scale Convolutional Network for Spatial Steganalysis performances on discriminating trivial perturbation introduced by spatial steganographic schemes. In this paper, we propose a deep residual multi-scale convolutional network model, which outperforms several CNN-based steganalysis schemes and hand-crafted rich models. Compared to CNN-based steganaly
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Provably Secure Generative Steganography Based on Autoregressive ModelConsequently, generative steganography, a novel steganographic method finishing the operation of steganography directly in the process of image generation, tends to get more attention. However, most of the existing generative steganographic methods have more or less shortcomings, such as low securit
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A Novel Steganalysis of Steghide Focused on High-Frequency Region of Audio Waveformonventional steganalysis utilize the statistics of high-frequency regions and silent temporal segments of the target signals intentionally or unintentionally. Moreover, the frequency components just below the Nyquist frequency are important for statistic-based steganalysis in terms of the signal-to-
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Cycle GAN-Based Attack on Recaptured Images to Fool both Human and Machineed to detect recaptured images. To counter these techniques, in this paper, we propose a method that can translate recaptured images to fake “original images” to fool both human and machines. Our method is proposed based on Cycle-GAN which is a classic framework for image translation. To obtain bett
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Spherical Panorama Image Watermarking Using Viewpoint Detectionnt a new watermarking technique to protect spherical panorama images as well as view-images that are rendered with a specific viewpoint. Solving the watermark synchronization problem in the detection process requires finding the viewpoint of a view-image. Scale Invariant Feature Transform (SIFT) and
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Improved High Capacity Spread Spectrum-Based Audio Watermarking by Hadamard Matriceson. In this paper, we use Hadamard sequences, which are rows of Hadamard matrices, to embed and extract watermarks instead of pseudonoise sequences. By exploiting the orthogonality of Hadamard sequences and a technique of sign change, we propose a new spread spectrum-based audio watermarking method.
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