SWT-DCT-Based Robust Watermarking for Medical Image

Dai, Qianning; Li, Jingbing; Bhatti, Uzair Aslam; Chen, Yen‐Wei; Liu, Jing

doi:10.1007/978-981-13-8566-7_9

Cited by 21 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, our target is to get a one-dimensional array, so we applied variance and get a vector of 1 × 524. Mathematically, the variance process is defined as follows: DCT is one of the old and most widely used techniques for extraction of features [22]. It has high information packing capability and thus is used for compression of images.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

Khan¹,

Alhaisoni²,

Nazir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 in Wuhan, China. After that, it quickly spread throughout the world and became a disease. It has significantly impacted our everyday lives, the national and international economies, and public health. However, early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system. Clinical radiologists primarily use chest X-rays, and computerized tomography (CT) scans to test for pneumonia infection. We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study. We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization. We begin by extracting standard features such as discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and dominant rotated local binary patterns (DRLBP). In addition, we extracted Shanon Entropy and Kurtosis features. In the following step, a Max-Covariance-based maximization approach for feature fusion is proposed. The fused features are optimized in the preliminary phase using Particle Swarm Optimization (PSO) and the ELM fitness function. For final prediction, PSO is used to obtain robust features, which are then implanted in a Support Vector Data Description (SVDD) classifier. The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients. These images are from the Radiopaedia website. For the proposed scheme, the fusion and selection process accuracy is 88.6% and 93.1%, respectively. A detailed analysis is conducted, which supports the proposed system efficiency.

show abstract

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

Khan¹,

Alhaisoni²,

Nazir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Therefore, the digital zero watermarking algorithms appear in the research idea of the researchers [4]. Many researchers in the field of image processing use hybrid transformation to solve the problem of leakage in medical image transmission [5][6][7][8]. Traditional digital watermarking schemes embed an invisible and detectable watermark in the host image to protect the image.…”

Section: Introductionmentioning

confidence: 99%

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Sheng¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key vector is obtained and saved at the same time. Similarly, the same feature extraction method is used to extract the deep features of the medical image to be tested and generate the feature vector. Later, the XOR operation is carried out between the feature vector and the logical key vector, and the encrypted watermark is extracted and decrypted to get the restored watermark; the normalized correlation coefficient (NC) of the original watermark and the restored watermark is calculated to determine the ownership and watermark information of the medical image to be tested. After calculation, most of the NC values are greater than 0.50. The experimental results demonstrate the algorithm's robustness, invisibility, and security, as well as its ability to accurately extract watermark information. The algorithm also shows good resistance to conventional attacks and geometric attacks.

show abstract