All-round improvement in DCT-based blind image watermarking with visual enhancement via denoising autoencoder

Hu, Hwai-Tsu; Hsu, Ling-Yuan; Lee, Tung-Tsun

doi:10.1016/j.compeleceng.2022.107845

Cited by 12 publications

(8 citation statements)

References 27 publications

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“…achieved with the use of the WPCC method, and after denoising, i.e. with the proposed and existing method [21], is shown in Figs. 5 and 6, respectively.…”

Section: /2023mentioning

confidence: 99%

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Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Pulgam¹,

Shinde

2023

JTIT

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Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), different watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more difficult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The ﬁndings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying specific diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy.

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“…achieved with the use of the WPCC method, and after denoising, i.e. with the proposed and existing method [21], is shown in Figs. 5 and 6, respectively.…”

Section: /2023mentioning

confidence: 99%

“…Gaussian LPF Gaussian filter Speckle noise Proposed SDCNN WPCC method [17] Existing method [21] Fig. 6.…”

Section: Average Filter Median Filter Motion Blurmentioning

confidence: 99%

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Pulgam¹,

Shinde

2023

JTIT

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“…Hidden layer features were obtained by using DAE. It is a robust variation of AE, in which a stochastically corrupted version of the input is used to feed the AE (Gaussian additive noise with zero means), while the uncorrupted input is still used as the target for the optimization of the parameters [28,29]. The general idea is depicted in Figure 5.…”

Section: Avoiding Overfittingmentioning

confidence: 99%

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network

Cui

Hao

et al. 2022

Foods

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Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

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“…To perform watermarking, transform-domain methods usually convert the host image into a transformed domain first, and then hide the watermark information by modifying the transformed coefficients. Commonly employed transformations for image watermarking include discrete Fourier transform (DFT) (Fares et al 2020;Hsu and Hu 2020; Urvoy et al 2014), discrete wavelet transform (DWT) (Barni et al 2001;Hu and Hsu 2017;Huynh-The et al 2016), DCT (Hsu and Hu 2017;Hu et al 2022;Moosazadeh and Ekbatanifard 2019;Patra et al 2010;Singh and Bhatnagar 2018), matrix decomposition (MD) (Ali et al 2015;Chang et al 2005;Hsu and Tu 2020;Koley 2021;Nha et al 2022;Su et al 2020), and combinations of these transforms (Hu and Hsu 2015;Kang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Blind color image watermarking incorporating a residual network for watermark denoising and super-resolution reconstruction

Hsu²

2022

Soft Comput

Self Cite

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Watermarking is a technique for hiding secret information in various types of multimedia data to protect intellectual property rights. Currently, the integration of deep learning technology with image watermarking is reshaping the application and promotion of relevant techniques developed so far. This paper presents a novel type of blind color image watermarking method that embeds a downsized color image into a host color image. Watermarking implementation involves partitioning the host image into non-overlapping blocks of 8 × 8 pixels, performing discrete cosine transform (DCT) for each block of every channel, and then manipulating the magnitudes of three designated DCT coefficients subject to a minimization constraint. The experimental results confirmed that the proposed image watermarking method outperformed four other methods in terms of zero-normalized cross-correlation (ZNCC). Moreover, watermark imperceptibility, as reflected by the measured peak signal-to-noise ratio and mean structural similarity metrics, remained at a satisfactory level. In addition to this new style of color image watermarking, we employed a deep residual network to reduce noise and increase the resolution of the retrieved watermarks. Overall, the residual network achieved a satisfactory ZNCC level (>0.88) when the watermark images were super-resolved by a factor of sixteen.

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All-round improvement in DCT-based blind image watermarking with visual enhancement via denoising autoencoder

Cited by 12 publications

References 27 publications

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network

Blind color image watermarking incorporating a residual network for watermark denoising and super-resolution reconstruction

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