Image watermarking using soft computing techniques: A comprehensive survey

Singh, Om Prakash; Singh, Amit Kumar; Srivastava, Gautam; Kumar, Neeraj

doi:10.1007/s11042-020-09606-x

Cited by 61 publications

(34 citation statements)

References 124 publications

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“…Chebyshev polynomial [14] are used to generate chaotic sequence and define as (2) Where C_n (m):[-1,1]→[-1,1] is n-order Chebyshev polynomial. For n >2 the map turns chaotic and nonlinear.…”

Section: Chebyshev Mapmentioning

confidence: 99%

“…Nowadays, there is an increasing tendency to upload images to online platforms acting as information carriers for various applications, such as social media, telemedicine, remote education and commercial and military uses [1][2]. Unfortunately, the unauthorized utilization of such images is a serious concern that significantly impacts security and privacy [3].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Encryption–Compression-based Algorithm for Securing Digital Images21

Singh

2023

J. Data and Information Quality

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Nowadays, there is an increasing tendency to upload images to online platforms acting as information carriers for various applications. Unfortunately, the unauthorized utilization of such images is a serious concern that has significantly impacted security and privacy. Although digital images are widely available, the storage of these images requires a large amount of data. This study aims to address these issues by developing an improved encryption–compression-based algorithm for securing digital images that reduces unnecessary hardware storage space, transmission time and bandwidth demand. First, the image is encrypted using chaotic encryption. Then the encrypted image is compressed using wavelet-based compression in order to make efficient use of resources without any information about the encryption key. On the other side, the image is decompressed and decrypted by the receiver. The security assessment of the proposed algorithm is performed in different ways, such as differential and statistical, key sensitivity and execution time analysis. The experimental analysis proves the security of the method against various possible attacks. Furthermore, the extensive evaluations on a real dataset demonstrate that the proposed solution is secure and has a low encryption overhead compared to similar methods.

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Section: Chebyshev Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Encryption–Compression-based Algorithm for Securing Digital Images21

Singh

2023

J. Data and Information Quality

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“…For Watermarking, it is one measure among others to have a good defense against copying [18]. It's a method for embedding data into a multimedia element such as an image, audio, or video file [19].…”

Section: Watermarking For Foveal and Visual Image Coding To Evaluate ...mentioning

confidence: 99%

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Allali¹,

Morino²,

Oussous³

et al. 2023

Information Security and Privacy in the Digital World - Some Selected Topics

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To ensure copyright protection and authenticate ownership of media or entities, image watermarking techniques are utilized. This technique entails embedding hidden information about an owner in a specific entity to discover any potential ownership issues. In recent years, several authors have proposed various ways to watermarking. In computational intelligence contexts, however, there are not enough research and comparisons of watermarking approaches. Soft computing techniques are now being applied to help watermarking algorithms perform better. This chapter investigates soft computing-based image watermarking for a medical IoT platform that aims to combat the spread of COVID-19, by allowing a large number of people to simultaneously and securely access their private data, such as photos and QR codes in public places such as stadiums, supermarkets, and events with a large number of participants. Therefore, our platform is composed of QR Code, and RFID identification readers to ensure the validity of a health pass as well as an intelligent facial recognition system to verify the pass’s owner. The proposed system uses artificial intelligence, psychovisual coding, CoAP protocol, and security tools such as digital watermarking and ECC encryption to optimize the sending of data captured from citizens wishing to access a given space in terms of execution time, bandwidth, storage space, energy, and memory consumption.

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“…Soft computing techniques proposed in [ 18 ] are used to improve performance of watermarking algorithms and applications of the same. Soft computing techniques also have been adopted for segmenting images and synergistically provide malleable information competent enough to manipulate real-life circumstances.…”

Section: Related Workmentioning

confidence: 99%

Dental Images’ Segmentation Using Threshold Connected Component Analysis

Majanga

Viriri

2021

Computational Intelligence and Neuroscience

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Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93 % for both precision and recall values, respectively.

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Image watermarking using soft computing techniques: A comprehensive survey

Cited by 61 publications

References 124 publications

An Improved Encryption–Compression-based Algorithm for Securing Digital Images21

An Improved Encryption–Compression-based Algorithm for Securing Digital Images21

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Dental Images’ Segmentation Using Threshold Connected Component Analysis

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