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Related Concept Videos

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Videos

DeepLayer-ID: A Lightweight Multi-Domain Forensic Framework for Real-Time Deepfake Detection in Resource-Constrained

Nayef H Alshammari1, Sami Aziz Alshammari2

  • 1Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

DeepLayer-ID effectively detects deepfakes in degraded Unmanned Aerial Vehicle (UAV) imagery by analyzing spatial, frequency, and residual domains. This robust framework ensures data integrity for critical UAV applications.

Keywords:
UAV forensicsaerial surveillancedeepfake detectionfrequency-domain forensicsmulti-channel feature decompositionreal-time detectionresidual noise analysistransformer fusion

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Digital Forensics
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicle (UAV) imaging systems are vital for surveillance and monitoring.
  • Deepfake technology poses a significant threat to the integrity of aerial sensor data.
  • Real-world degradations like motion blur and noise complicate deepfake detection in UAV imagery.

Purpose of the Study:

  • To introduce DeepLayer-ID, a novel forensic framework for detecting deepfakes in degraded UAV sensing environments.
  • To develop a degradation-aware system that addresses challenges in aerial data integrity.
  • To enhance the robustness of deepfake detection under heterogeneous acquisition conditions.

Main Methods:

  • Proposed a multi-domain forensic framework decomposing evidence into spatial, frequency, and residual domains.
  • Utilized discrete wavelet transform and high-pass residual filtering to capture inconsistencies and anomalies.
  • Employed a lightweight transformer-based fusion mechanism for adaptive integration of cross-domain representations.
  • Constructed a balanced dataset of 1096 aerial frames with synthetic manipulations and physics-consistent degradations.

Main Results:

  • DeepLayer-ID achieved 97.8% accuracy and 0.991 AUC, outperforming existing methods like ResNet-50 and XceptionNet.
  • The model demonstrated real-time feasibility with 5.4 M parameters and 9.8 ms inference latency.
  • The framework showed superior robustness under various synthetic manipulations and real-world degradations.

Conclusions:

  • Structured multi-domain signal decomposition with attention-guided fusion offers a robust solution for deepfake detection in degraded UAV systems.
  • DeepLayer-ID provides a computationally efficient and accurate method for ensuring the integrity of UAV visual data.
  • The findings highlight the potential of advanced forensic techniques in securing critical aerial sensing applications.