An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network

Haghighi, Behrouz Bolourian; Taherinia, Amir Hossein; Monsefi, Reza

doi:10.1007/s12559-019-09700-9

Cited by 25 publications

(10 citation statements)

References 44 publications

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“…Here, the BPFNN network is fed with an input having 𝑛𝑢𝑚𝑒𝑙(𝐸 𝐿𝑜𝑐 ) elements. For a compromise between complexity and performance, the size of 𝑛𝑢𝑚𝑒𝑙(𝐸 𝐿𝑜𝑐 )] was set within [6][7][8]. Here, the BFNN unit takes place in two stages:…”

Section: The Bpfnn Neural Networkmentioning

confidence: 99%

“…Robust watermarking can be utilized for copyright protection [5], where it is designed to resist any anticipated attacks that attempt to destroy or remove the watermark. Semi-fragile watermarking has the main feature of reconstructing tampered information, which allows a certain degree of robustness against various attacks [7]. Methods based on fragile watermarking are very sensitive to even minor modifications and are concerned with the integrity protection [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Al-Otum

2023

Preprint

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Image watermarking technologies have been developed to meet the concerns in multimedia copyright protection caused by the tremendous demand on multimedia sharing. In this work, a novel color image watermarking scheme is proposed for copyright protection applications. The developed scheme employs the wavelet packets decomposition (WPD) along with utilizing the back-propagation feed-forward neural networks (BPFNN). Here, a BPFNN-based color image watermarking using WPD decomposition approach is proposed and is denoted as FNN-WPD. The produced WPD-packets, from each of the RGB color channels, are reordered into chains having specific spatial- and frequency-based features. These chains are used for mark embedding and extraction using the BPFNN construction. Simulations have shown superior imperceptibility (PSNR ≥ 60 dB) and improved high robustness against incident and malicious attacks, while exhibiting low computational cost, especially at the extraction stage. Moreover, the scheme has been extended to be used for QR-code embedding with very promising robustness performance.

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Section: The Bpfnn Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Al-Otum

2023

Preprint

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“…The test results show the validity in terms of imperceptibility and performance of the proposed approach to secure and robust assaults. B. Bolourian Haghighi & et al [70], proposes a novel watermarking technique focused on (LWT) and (FNN) to detect and restore manipulation by hiding half-fragile data. The proposed approach outperforms similar works in terms of dominance, performance, and efficacy for applications including tamper detection and recovery.…”

Section: Extensive Research On Fragile Watermark As Follow;mentioning

confidence: 99%

“…But the most important DCT, DWT, and SVD technologies are used with hybrid methods for more image security. It should be noted that the robustness of data rates and imperceptibility also needs to be handled and that the optimal swap will depend on the application [43,64,[68][69][70]73].…”

Section: Assessments and Recommenda-tionsmentioning

confidence: 99%

Image Authentication Based on Watermarking Approach: Review

Hasan

Ameen

Hasan

2021

AJRCoS

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Digital image authentication techniques have recently gained a lot of attention due to their importance to a large number of military and medical applications, banks, and institutions, which require a high level of security. Generally, digital images are transmitted over insecure media, such as the Internet and computer networks of various kinds. The Internet has become one of the basic pillars of life and a solution to many of the problems left by the coronavirus. As a result, images must be protected from attempts to alter their content that might affect important decision-making. An image authentication (IA) system is a solution to this difficult problem. In the previous literature, several methods have been proposed to protect the authenticity of an image. Digital image watermark is a strategy to ensure the reliability, resilience, intellectual property, and validity of multimedia documents. Digital media, such as images, audio, and video, can hide content. Watermarking of a digital image is a mechanism by which the watermark is embedded in multimedia and the image of the watermark is retrieved or identified in a multimedia entity. This paper reviews IA techniques, watermark embedding techniques, tamper detection methods and discusses the performance of the techniques, the pros and cons of each technique, and the proposed methods for improving the performance of watermark techniques.

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“…From the experimental results, it is noted that the method provides better performance in terms of security, quality and robustness; however, the complexity and cost are high. Haghighi et al [27] implemented LWT, DCT and a feed-forward neural network-based semi-fragile scheme for temper detection and recovery. Using the inverse halftoning technique, the tempered regions are identi ed in the recovery stage.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization-Based Robust Watermarking Using Denoising Convolutional Neural Network

Mahto

Singh

2021

Preprint

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Colour images have been widely used in many aspects of life; however, copyright violation issues related to these images motivate research efforts. This paper aims to develop an enhanced watermarking algorithm for producing a watermarked image using hybrid optimisation with high imperceptibility and robustness. The algorithm is based on spatial and transform domains and begins by embedding multiple secret marks into cover media using an optimal scaling factor. The multi-type mark contributes an additional level of authenticity to the proposed algorithm. Furthermore, the marked image is encrypted using an improved encryption scheme, and the denoising convolutional neural network (DnCNN) is employed to enhance the robustness of the proposed algorithm. The results reveal that the proposed watermarking algorithm yields low computational overhead, excellent watermark capacity, imperceptibility, and robustness to common filtering attacks. Moreover, the comparison shows that the proposed algorithm outperforms other competing methods.

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An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network

Cited by 25 publications

References 44 publications

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Image Authentication Based on Watermarking Approach: Review

Hybrid Optimization-Based Robust Watermarking Using Denoising Convolutional Neural Network

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