Exploiting De-Noising Convolutional Neural Networks DnCNNs for an Efficient Watermarking Scheme: A Case for Information Retrieval

Rahim, Tariq; Khan, Saud; Usman, Muhammad Arslan; Shin, Soo Young

doi:10.1080/02564602.2020.1721342

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Specifically, there are 64 filters in each convolutional layer along with rectified linear units in the VDSR framework. Due to its advanced architecture, the VDSR has been adopted in different areas, such as network security [22], machinery [23], seismic analysis [24] and image communications [25]. It is worth noticing that the LR images with reduced dimensions save the transmission bandwidth considerably.…”

Section: Related Workmentioning

confidence: 99%

A Secure Cooperative Transmission of Image Super-Resolution in Wireless Relay Networks

Duong,

Phan,

Vien

et al. 2023

Electronics

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The image transmission over wireless media experiences not only unavailable performance loss caused by the environment and hardware issues, but also information leakage to eavesdroppers who can overhear and attempt to recover the images. This paper proposes a secure cooperative relaying (SCR) protocol for the image communications in wireless relay networks (WRNs) where Alice sends high-resolution (HR) images to Bob with the assistance of a relaying user named Relay, and in the presence of an eavesdropper named Eve. In order to enhance the security of the image communications, random linear network coding (RLNC) is employed at both Alice and Relay to conceal the original images from Eve with RLNC coefficient matrices and reference images in the shared image datastore. Furthermore, the original HR images are downscaled at Alice to save transmission bandwidth and image super-resolution (ISR) is adopted at Bob due to its capability to recover the HR images from their low-resolution (LR) version, while still maintaining the image quality. In the proposed SCR protocol, Bob can decode both the original images transmitted from Alice over the direct link and the images forwarded by Relay over the relaying links. Simulation results show that the SCR protocol achieves a considerably higher performance at Bob than at Eve since Eve does not know the coefficient matrices and reference images used at Alice and Relay for the RLNC. The SCR protocol is also shown to outperform the counterpart secure direct transmission protocol without the relaying links and secure relaying transmission without the direct link. Additionally, an increased scaling factor can save the transmission bandwidth for a slight change in the image quality. Moreover, the impacts of direct, relaying and wiretap links are evaluated, verifying the effectiveness of the SCR protocol with the employment of Relay to assist the image communications between Alice and Bob in the WRNs.

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Section: Related Workmentioning

confidence: 99%

A Secure Cooperative Transmission of Image Super-Resolution in Wireless Relay Networks

Duong,

Phan,

Vien

et al. 2023

Electronics

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“…Wang et al [25] proposed a cascaded CNN algorithm, which identifies the static and dynamic flame features with two independent CNNs, respectively, and judged whether an image area is flame combining the results of the two networks [26]. Rahim et al [27] designed a DNCNN for fire image recognition, and compared it with VGGNet and ZFNet. All three network models can accurately recognize single-view images, but perform poorly on multi-source samples.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where, f() is the sigmoid function of the classifier [27]; yi is the label of the i-th library image pair; l means the detection image has the same label as the i-th library image.…”

Section: Graph Representation and Node Featuresmentioning

confidence: 99%

Forest Fire Recognition Based on Feature Extraction from Multi-View Images

Wu¹,

Zhang²,

Ji³

et al. 2021

116

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Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

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“…denoising with appealing run time. That is why since its introduction, it has been adopted in various fields, such as seismic [18], machinery [19], as well as network security [20].…”

Section: Related Work a Super Resolutionmentioning

confidence: 99%