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

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Net Change Theorem01:22

Net Change Theorem

The Net Change Theorem is a fundamental principle in calculus that establishes a direct relationship between a function’s rate of change and its accumulated change over an interval. Mathematically, it states that the definite integral of a function's derivative over a given interval [a,b] yields the net change in the original function:This theorem has significant applications in various real-world scenarios, including physics, economics, and engineering. A particularly useful application is in...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...

You might also read

Related Articles

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

Sort by
Same author

Doping Manipulation of Donor/Acceptor by Perovskite Quantum Dots Enables >20.5% Organic Nonfullerene Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Achieving Maximum Chirality and Enhancing Third-Harmonic Generation via Quasi-Bound States in the Continuum in Nonlinear Metasurfaces.

Nanomaterials (Basel, Switzerland)·2026
Same author

Sugarcane Breeding in the Genomic Era: Integrative Strategies and Emerging Technologies.

Plants (Basel, Switzerland)·2026
Same author

In-situ entropic ligand engineering enables high-efficiency quantum dot solar cells.

Nature communications·2025
Same author

Fluorinated Pseudo-Halide Anion Enables >19% Efficiency and Durable Perovskite Quantum Dot Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Hydrogel-Based Therapeutics for Diabetic Oral Wounds: From Mechanisms to Applications.

Advanced healthcare materials·2025

Related Experiment Videos

RAN: A randomness-anchored watermark attacking network with stealth and effectiveness.

Fan Li1, Du Li2, Kunqi Li3

  • 1College of Artificial Intelligence, Chengdu University of Information Technology, Chengdu, 610225, China.

Scientific Reports
|May 9, 2026
PubMed
Summary

This study introduces a novel deep learning network (RAN) that attacks digital watermarks by converting them into random noise. This method effectively disrupts watermarks while preserving image quality, advancing robust watermarking research.

Keywords:
ImperceptibilityRandomnessWatermark attacking

Related Experiment Videos

Area of Science:

  • Computer Science
  • Digital Image Processing
  • Machine Learning

Background:

  • Robust watermarking is crucial for digital content security.
  • Deep learning methods are emerging for watermark attacks, balancing imperceptibility and disruption.
  • Existing methods often rely on original watermarks for attack training.

Purpose of the Study:

  • To propose a novel deep learning-based watermark attacking network named RAN (Randomness-Anchored Attacking Network).
  • To introduce an alternative watermark loss function that anchors attacks to randomness instead of original watermarks.
  • To evaluate the effectiveness of RAN in disrupting watermarks while maintaining visual fidelity.

Main Methods:

  • Development of the Randomness-Anchored Attacking Network (RAN).
  • Implementation of a novel watermark loss function based on randomness anchoring.
  • Extensive experimental comparisons with existing watermark attacking schemes.

Main Results:

  • RAN models demonstrate competitive attacking ability.
  • The proposed method effectively preserves the visual fidelity of attacked watermarked images.
  • RAN achieves satisfying performance in watermark disruption.

Conclusions:

  • The proposed RAN offers a new approach for designing stealthy and effective deep learning-based watermark attacks.
  • This research provides significant implications for the development of more robust watermarking techniques.
  • The randomness-anchored loss function presents a novel strategy for watermark attack optimization.