An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection

Rai, Manish; Goyal, Sachin; Pawar, Mahesh

doi:10.1007/s00034-023-02299-1

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…In [68], an optimized deep fusion convolutional neural network (FCNN)-based digital color image watermarking scheme was proposed for copyright protection. It suggests a deep fusion CNN that uses an optimization method as its basis.…”

Section: Classification Of Deep Learning-based Image Watermarking Sch...mentioning

confidence: 99%

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Ben Jabra,

Ben Farah

2024

Circuits Syst Signal Process

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The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking.

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Section: Classification Of Deep Learning-based Image Watermarking Sch...mentioning

confidence: 99%

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Ben Jabra,

Ben Farah

2024

Circuits Syst Signal Process

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“…Color imagery refers to the subjective emotions associated with specific colors, such as emotions, images, and symbols [9][10]. Color emotion refers to the influence of mood, atmosphere and personality conveyed in a product or environment [11][12].…”

Section: Color Imagementioning

confidence: 99%

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

Han

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Color is a visual element of people, it can bring people different levels, different feelings, and can make people have associations and emotional resonance. Under the background of Kansei engineering, product color design is an important research field. This paper aims to discuss the predictive model of Kansei engineering in product color design, in order to improve the quality of products and meet the needs of consumers. Through empirical research and user survey, this paper verifies the accuracy and effectiveness of product color perceptual image generated by machine vision prediction model for the elderly products. The survey and experimental results show that support vector machines perform best, with an accuracy of 91% and a recall rate 4% higher than BP neural networks.

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“…This is an explanation of how DNNs are applied to secure image security. Rai et al, (2023) suggested the ECO algorithm is designed to maximize the trade-off between imperceptibility the degree to which the watermark is visible or degrades the quality of the image and robustness, or the watermark's capacity to survive different types of attacks. It establishes the watermark's ideal strength factor.…”

Section: Tuijinjishu/journal Of Propulsion Technology Issn: 1001-4055...mentioning

confidence: 99%

A Review Study on various Image Security Techniques and Emerging trends for Visual Data Protection

Dr. K. Rajeswari, Mrs. Dipali Ghatge

2023

tjjpt

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In the rapidly evolving digital age, the proliferation of visual data and images has led to increased concerns about their security and protection. This paper reviews various image security techniques and emerging trends aimed at safeguarding visual data in a digital landscape. The research explores the evolution of image security measures and their adaptation to address contemporary challenges in data protection. This study introduces the stage by elucidating the multifaceted nature of image security, emphasizing its significance in preserving the confidentiality and integrity of visual data. The complex landscape of image security threats is examined, highlighting the challenges faced in safeguarding visual data from various vulnerabilities. This paper delves into the need and significance of image security in the digital age, presenting a compelling case for the adoption of robust security measures. It explores the risks associated with image theft and tampering in the online world and underscores the essential role of image encryption and authentication methods in countering these threats. The study then delves into the realm of encryption techniques, offering insights into both symmetric and asymmetric cryptographic algorithms designed to enhance image security on the internet. The research extends its focus to secure data hiding techniques, such as reversible data hiding in encrypted images, histogram shifting-based data hiding, and high-embedding capacity methods employing optimization algorithms. The paper also explores digital watermarking as a means of image authentication and copyright protection, along with image tamper detection and self-recovery mechanisms in watermarking schemes. Additionally, this review assesses the impact of emerging trends, including machine learning, Blockchain, and AI-based solutions, on the security of visual data, such as deep neural networks, generative adversarial neural networks, and their role in image steganography, forensics, and privacy protection. Consequently, by providing an in-depth analysis of the current state of image security and future directions, this study contributes to the understanding of the critical role played by image security in the digital age and its implications for data protection and privacy.

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An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection

Cited by 10 publications

References 33 publications

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

A Review Study on various Image Security Techniques and Emerging trends for Visual Data Protection

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