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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy.

Yu-Hsiu Lin1, Chih-Hsien Hsia2, Bo-Yan Chen3

  • 1Dept. Electrical Engineering, Allied AI Biomedical Research Center, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan. yhlin1108@stust.edu.tw.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces a novel data hiding scheme for compressed images using Absolute Moment Block Truncation Coding (AMBTC). The method enhances visual quality and security for the Internet of Things (IoT) in smart cities.

Keywords:
Visual Internet of Thingsabsolute moment block truncation coding (AMBTC)data hidingsecure image transmissionvisual sensing data security

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Area of Science:

  • Computer Science
  • Information Security
  • Image Processing

Background:

  • Visual Internet of Things (IoT) is crucial for smart cities, necessitating secure data transmission.
  • Image compression is vital for efficient data transfer but poses security challenges.
  • Data hiding in digital images is essential for authentication, forgery prevention, and intellectual property protection.

Purpose of the Study:

  • To develop a novel data hiding scheme for compressed images using Absolute Moment Block Truncation Coding (AMBTC).
  • To improve the visual quality of data-embedded compressed images by leveraging human vision system properties.
  • To enhance security and transmission efficiency for visual data in the Internet of Things (IoT) context.

Main Methods:

  • A two-phase data hiding strategy is proposed for AMBTC compressed images.
  • An intra-block embedding phase manipulates AMBTC parameters using a novel hidden function.
  • An inter-block embedding phase reversibly embeds data by exploiting adjacent block value relevance.
  • A direct binary search halftoning scheme is integrated to enhance image quality without altering fixed parameters.
  • Data extraction is performed using the modulo operator.

Main Results:

  • The proposed method effectively embeds secret data within AMBTC compressed images.
  • Experimental results demonstrate superior visual quality compared to existing methods.
  • The scheme enhances network transmission efficiency by hiding data in compressed images.
  • The method proves effective for increasing IoT security.

Conclusions:

  • The developed data hiding scheme offers an effective solution for securing compressed images.
  • The integration of human vision properties and AMBTC improves visual quality and security.
  • This research contributes to more efficient and secure data handling in visual IoT applications.