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This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model.

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Recently pixel pairing and pixel sorting/selection have been used in prediction-error expansion based reversible data hiding schemes to generate low entropy prediction-error histograms (PEH) necessary for achieving high fidelity. Such schemes generally use the four-neighbor average rhombus predictor as it allows pixel sorting and flexible pixel pairing. In this paper, we propose the maximally separated averages (MSA) predictor that uses the four-neighborhood context.

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15 Views

Training deep neural networks is a computationally expensive task. Furthermore, models are often derived from proprietary datasets that have been carefully prepared and labelled. Hence, creators of deep learning models want to protect their models against intellectual property theft. However, this is not always possible, since the model may, e.g., be embedded in a mobile app for fast response times. As a countermeasure watermarks for deep neural networks have been developed that embed secret information into the model.

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19 Views

The method reported here realizes an inaudible echo-hiding based speech watermarking by using sparse subspace clustering (SSC). Speech signal is first analyzed with SSC to obtain its sparse and low-rank components. Watermarks are embedded as the echoes of the sparse component for robust extraction. Self-compensated echoes consisting of two independent echo kernels are designed to have similar delay offsets but opposite amplitudes. A one-bit watermark is embedded by separately performing the echo kernels on the sparse and low-rank components.

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50 Views

Recent studies have shown that convolutional neural networks (CNNs) can boost the performance of audio steganalysis. In this paper, we propose a well-designed fully CNN architecture for MP3 steganalysis based on rich high-pass filtering (HPF). On the one hand, multi-type HPFs are employed for "residual" extraction to enlarge the traces of the signal in view of the truth that signal introduced by secret messages can be seen as high-pass frequency noise.

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73 Views

Pixel-value-ordering (PVO) appears as an efficient technique for high-fidelity reversible data hiding. This paper proposes a reversible data hiding scheme based on the pairwise PVO framework with improved difference equations. Both the pixel pair selection and the embedding algorithms are also streamlined. The proposed scheme uses a block classification approach based on a local complexity metric. Uniform blocks are processed using the proposed improved pairwise PVO algorithm. Slightly noisy blocks are embedded using a classic PVO scheme and noisy blocks are kept unchanged.

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42 Views

This paper proposes a new vacating room after encryption reversible data hiding scheme developed for color images. The proposed scheme uses standard exclusive-or encryption and inherits the main features of vacating room after encryption schemes, namely joint and separate methods for data embedding. The proposed scheme exploits both the correlation between neighboring pixels and the correlation between color channels by predicting the original pixel values on color channel differences.

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41 Views

3D steganalysis aims to find the changes embedded through steganographic or information hiding algorithms into 3D models. This research study proposes to use new 3D features, such as the edge vectors, represented in both Cartesian and Laplacian coordinate systems, together with other steganalytic features, for improving the results of 3D steganalysers. In this way the local feature vector used by the steganalyzer is extended to 124 dimensions.

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13 Views

In this paper, we propose the first joint watermarking-encryption-compression scheme for the protection of medical images. Its originality is twofold. In a first time, it allows the access to watermarking-based security services from the encrypted and the compressed bitstreams without having to parse them even partially. It becomes possible to trace images and control their reliability (i.e. integrity and authenticity) from both the encrypted and compressed domains.

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33 Views

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