Digital image encryption with validation by ECC and embedding at low frequency region using the genetic approach

Abstract—In the current internet era, the security of digital images has become increasingly important due to their numerous applications and uses. Although many researchers have proposed end-to-end security and authenticity against various attacks, achieving security, validation, and robustness together has been a challenge. This paper proposes a model called Low-Frequency Embedding and Elliptical Curve Cryptography (LFE-ECC), which provides all the necessary requirements for image security. The proposed model achieves image validation for the authentic sender by embedding a secret signature in the low-frequency region of the image. The robustness of the image is achieved by embedding a secret signature at a selected coefficient of the DWT feature. The moth flame optimization genetic algorithm is used for coefficient selection, and the additional security of embedded images is achieved using the elliptical curve cryptography technique. ECC provides encryption and validation for both parties. An experiment is conducted on real and artificial datasets under ideal and attack environments, and the results demonstrate the improved performance of the proposed LFE-ECC model against a range of attacks.

Institutions

Publication Details

Journal
International Journal of Electronics and Telecommunications
Published
2025-04-07
DOI
https://doi.org/10.24425/ijet.2025.153549
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital image encryption with validation by ECC and embedding at low frequency region using the genetic approach

International Journal of Electronics and Telecommunications
Chaos-based Image/Signal Encryption
article

Digital image encryption with validation by ECC and embedding at low frequency region using the genetic approach

article en

Abstract

Abstract—In the current internet era, the security of digital images has become increasingly important due to their numerous applications and uses. Although many researchers have proposed end-to-end security and authenticity against various attacks, achieving security, validation, and robustness together has been a challenge. This paper proposes a model called Low-Frequency Embedding and Elliptical Curve Cryptography (LFE-ECC), which provides all the necessary requirements for image security. The proposed model achieves image validation for the authentic sender by embedding a secret signature in the low-frequency region of the image. The robustness of the image is achieved by embedding a secret signature at a selected coefficient of the DWT feature. The moth flame optimization genetic algorithm is used for coefficient selection, and the additional security of embedded images is achieved using the elliptical curve cryptography technique. ECC provides encryption and validation for both parties. An experiment is conducted on real and artificial datasets under ideal and attack environments, and the results demonstrate the improved performance of the proposed LFE-ECC model against a range of attacks.

International Journal of Electronics and Telecommunications
National Institute of Technology Raipur (IN)
Openalex Percentile: Top 95%
Chaos-based Image/Signal Encryption
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Digital image encryption with validation by ECC and embedding at low frequency region using the genetic approach · International Journal of Electronics and Telecommunications (2025) | TGRS Research Map | TGRS