Secure and reversible fragile watermarking for accurate authentication and tamper localization in medical images

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110072 - 110072

Опубликована: Янв. 29, 2025

Язык: Английский

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Digital to quantum watermarking: A journey from past to present and into the future

Computer Science Review, Год журнала: 2024, Номер 54, С. 100679 - 100679

Опубликована: Сен. 14, 2024

Язык: Английский

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Detection of Manipulations in Digital Images: A Review of Passive and Active Methods Utilizing Deep Learning

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 881 - 881

Опубликована: Янв. 17, 2025

The modern society generates vast amounts of digital content, whose credibility plays a pivotal role in shaping public opinion and decision-making processes. rapid development social networks generative technologies, such as deepfakes, significantly increases the risk disinformation through image manipulation. This article aims to review methods for verifying images’ integrity, particularly deep learning techniques, addressing both passive active approaches. Their effectiveness various scenarios has been analyzed, highlighting their advantages limitations. study reviews scientific literature research findings, focusing on techniques that detect manipulations localize areas tampering, utilizing statistical properties images embedded hidden watermarks. Passive methods, based analyzing itself, are versatile can be applied across broad range cases; however, depends complexity modifications characteristics image. Active which involve embedding additional information into image, offer precise detection localization changes but require complete control over creating distributing visual materials. Both approaches have applications depending context available resources. In future, key challenge remains resistant advanced generated by diffusion models further leveraging innovations protect integrity content.

Язык: Английский

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Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement

Engineering Technology & Applied Science Research, Год журнала: 2024, Номер 14(3), С. 14725 - 14731

Опубликована: Июнь 1, 2024

In this paper, a novel approach to enhance image quality in real-time using Deep Reinforcement Learning (DRL) is introduced. The adopted method utilizes Convolutional Neural Network (CNN) within Q-learning framework dynamically apply various enhancement filters. These filters are selected based on their impact the Structural Similarity Index Measure (SSIM), which serves as primary metric for evaluating enhancements. effectiveness of proposed demonstrated through extensive experiments, where improvements measured by employing metrics such SSIM, Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). results exhibit significant potential DRL automating complex image-processing tasks real-world applications.

Язык: Английский

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A novel dynamic image watermarking technique with features inspired by quantum computing principles

AIP Advances, Год журнала: 2024, Номер 14(4)

Опубликована: Апрель 1, 2024

This research proposes a novel dynamic image watermarking technique with features inspired by quantum computing principles. method encodes binary values into qubits and embeds watermark an original image. The process is achieved utilizing circuits to manipulate the representing pixel of images. To extract watermark, encode each value qubit, combine them using operations, then measure resultant state. ensures integrity authenticity embedding that can be extracted high fidelity. Simulation results show our successfully watermarks while maintaining picture quality. Moreover, this exhibits robustness against common processing attacks, highlighting its potential for secure verification applications.

Язык: Английский

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Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 963 - 963

Опубликована: Янв. 19, 2025

Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing based on grayscale gradient computation still need to be optimized practicality, especially terms of actual visual quality and sensitivity image contrast. To optimize the visualization enhance robustness contrast changes, we propose Contrast Invariant Detection (CIED) method. CIED combines Gaussian filtering morphological processing preprocess images. It utilizes three Most Significant Bit (MSB) planes binary images detect extract edge information. Each bit plane is used edges 3 × blocks by proposed algorithm, then information from each fused obtain an image. This method generalized common types Since eliminates complex pixel operations, it faster more efficient. In addition, insensitive changes contrast, making flexible its application. comprehensively evaluate performance CIED, develop a dataset conduct evaluation experiments these The results show that average precision 0.408, recall 0.917, F1-score 0.550. indicate not only practical effects but also robust invariance. comparison with other confirm advantages CIED. study provides novel approach for within

Язык: Английский

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Watermarking Algorithm for Remote Sensing Images Based on Ring-Shaped Template Watermark and Multiscale LCM

Remote Sensing, Год журнала: 2024, Номер 16(14), С. 2535 - 2535

Опубликована: Июль 10, 2024

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose novel algorithm that integrates ring-shaped watermark with multiscale local contrast measure (LCM) method. In embedding stage, embedded into discrete Fourier transform (DFT) magnitude coefficients, converting small targets DFT domain. During detection LCM, classic infrared target method, enhances these and generates map. Peak then performed on map to determine radius of watermark. Finally, circular edge binarization applied extract information. The proposed method enables synchronization recovery blind conditions. experimental results demonstrate possesses strong robustness against various attacks such as rotation, scaling, translation, cropping. It outperforms comparative algorithms terms also exhibits good imperceptibility.

Язык: Английский

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Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

International Journal of Computers and Applications, Год журнала: 2024, Номер 46(8), С. 580 - 603

Опубликована: Июль 24, 2024

In today's digital landscape, the pervasive use of images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation these underscores critical need develop robust methods detecting tampering ensuring integrity. Fragile watermarking been found have extensive applications tamper detection recovery. An image technique detection, correction, recovery is presented in this study. proposed method employs a self-embedding generate reference watermark from original image, which advantage superior localization, capabilities, robustness against attacks. Set Partitioning Hierarchical Tree (SPIHT) algorithm applied image. Low-Density Parity Check (LDPC) employed error providing higher-quality reconstruction recover Schur decomposition processed watermarked blocks authentication bits each block enhance detection. was evaluated using PSNR, SSIM, BER, NC metrics grayscale colored images. demonstrated high various Comparative analysis with existing shows efficacy method.

Язык: Английский

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TCBR and TCBD: Evaluation metrics for tamper coincidence problem in fragile image watermarking

Engineering Science and Technology an International Journal, Год журнала: 2024, Номер 56, С. 101790 - 101790

Опубликована: Авг. 1, 2024

This paper proposed two evaluation metrics of the tamper coincidence in a block map design for image watermarking. These are called Tamper Coincidence Block Ratio (TCBR) and Density (TCBD). A occurred authentication self-recovery when recovery data original location were tampered with simultaneously. high limits inpainting's capability to recover region, leading an imprecise recovered image. The ratio density may significantly affect final quality. Previously, researchers mentioned their experiment but did not evaluate it any metrics. They evaluated robustness technique based on quality using Peak Signal-to-Noise (PSNR) Structural Similarity Index Measure (SSIM). coincidences primarily affected by implemented researcher. Thus, TCBR TCBD provide valuable insight into design's effectiveness preventing coincidence. experimental result shows that values inversely proportional value leads low Therefore, this will help effective minimizing obtain highest

Язык: Английский

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TDSF: Two-phase tamper detection in semi-fragile watermarking using two-level integer wavelet transform

Engineering Science and Technology an International Journal, Год журнала: 2024, Номер 61, С. 101909 - 101909

Опубликована: Дек. 3, 2024

Язык: Английский

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