Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

399
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
399
Convolution Properties I01:20

Convolution Properties I

236
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
236
Convolution Properties II01:17

Convolution Properties II

281
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
281
Deconvolution01:20

Deconvolution

251
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
251
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

5.5K
When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...
5.5K
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Recent Advances in the Diagnosis and Management of Cardiovascular Diseases: A Comprehensive Review.

Cureus·2026
Same author

Bridging the Gap: Promoting Adverse Drug Reaction Reporting through Innovative Strategies in Zero-reporting Zones at Karaikal - An Educational Intervention Study at Adverse Drug Reaction Monitoring Center.

Journal of research in pharmacy practice·2026
Same author

Deep inception neural network with residual connections for Tamil handwritten character recognition.

Scientific reports·2026
Same author

Dynamic SG-SKRDX hybrid framework for precision weather forecasting and crop suitability in the Cauvery Delta.

Scientific reports·2025
Same author

A custom hash algorithm for hosting secure gray scale image repository in public cloud.

Scientific reports·2025
Same author

Development of game theoretic hypergraph based autoencoder scheme for multiple objects tracking and anomaly detection for surveillance videos.

Scientific reports·2025

Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

Convolutional neural network and wavelet composite against geometric attacks a watermarking approach.

C Lakshmi1, C Nithya1, R Sivaraman2

  • 1School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India.

Scientific Reports
|August 26, 2025
PubMed
Summary

This study introduces a novel three-layer image watermarking technique using Discrete Wavelet Transform (DWT) for secure authentication in e-governance. The method embeds encrypted logos and owner identity, demonstrating high robustness and transparency with Convolutional Neural Network (CNN) restoration.

Keywords:
DWTDenoising convolutional neural network (DCNN)Fuzzy hashImage processing attacksLossless compression techniquesRobust watermarkingSingular value decomposition (SVD)

Related Experiment Videos

Last Updated: Sep 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

Area of Science:

  • Digital watermarking
  • Information security
  • Image processing

Background:

  • E-governance systems require robust ownership identification for services like banking, healthcare, and insurance.
  • Traditional watermarking methods face challenges in balancing payload capacity, robustness, and perceptual transparency.
  • Authentication is critical for secure data exchange in digital environments.

Purpose of the Study:

  • To propose a novel three-layer, feature-dependent image watermarking scheme in the transform domain.
  • To enhance security and authentication in digital e-governance applications.
  • To achieve high payload capacity, perceptual transparency, and robustness against various attacks.

Main Methods:

  • A three-layer watermarking approach using Discrete Wavelet Transform (DWT) was developed.
  • Singular Values of an encrypted logo, arithmetic coding for textual signatures, and run-length coding for owner identity were embedded in different DWT decomposition levels.
  • Feature extraction and data compression techniques were utilized for embedding.
  • Convolutional Neural Network (CNN) was employed for cover image reversibility and watermark extraction.
  • Attacks including Gaussian noise, salt and pepper noise, rotation, and cropping were simulated.

Main Results:

  • The proposed scheme achieved high perceptual transparency, with Structural Similarity Index Measure (SSIM) and Normalised Correlation (NC) approaching unity.
  • Robustness against various attacks was demonstrated, with a Peak Signal-to-Noise Ratio (PSNR) of approximately 44 dB.
  • High SSIM (>0.99) and NC (~1.0) values confirmed enhanced perceptual transparency and robustness.
  • Minimal bit error rates post-attack indicated reliable watermark recovery with CNN-aided restoration.
  • Low Mean Square Error (MSE) was achieved during cover image recovery.

Conclusions:

  • The developed DWT-based watermarking scheme effectively embeds a heavy payload while maintaining high perceptual transparency and robustness.
  • The integration of CNN significantly enhances the scheme's resilience against diverse attacks and aids in accurate image restoration.
  • This approach offers a promising solution for secure authentication in e-governance and other sensitive digital applications.