Key-Free Image Encryption Algorithm Based on Self-Triggered Gaussian Noise Sampling

With the emergence of the big data era, data privacy has become an increasingly important concern. Digital image, as one of the most prevalent forms of data, frequently contains sensitive information. Thus, ensuring the protection of this information has become a pressing issue. Consequently, resear...

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Veröffentlicht in:IEEE access Jg. 12; S. 153274 - 153284
Hauptverfasser: Gao, Kai, Chang, Chin-Chen, Lin, Chia-Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Big Data
Complexity
Decoding
denoising diffusion probabilistic model
Digital images
Digital imaging
Encryption
Image analysis
Image encryption
Image enhancement
Image quality
Image transmission
key-free
Noise reduction
Privacy
Random noise
Security
self-embedding
Streaming media
Thumbnail icons
Training
Visualization
White noise
ISSN:2169-3536, 2169-3536
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Zusammenfassung:With the emergence of the big data era, data privacy has become an increasingly important concern. Digital image, as one of the most prevalent forms of data, frequently contains sensitive information. Thus, ensuring the protection of this information has become a pressing issue. Consequently, researchers have been exploring various encryption algorithms to protect the privacy of image data effectively. However, traditional image encryption algorithms often face significant limitations, such as the requirement for transmitting encryption secret keys, high computational complexity, or vulnerability to decryption errors. To overcome these problems, in this paper, we propose a key-free image encryption algorithm that offers low computational complexity and high security. In addition, we use a pre-trained decoder to improve the visual quality of the embedded secret thumbnail image. Experimental results show that the proposed scheme exhibits strong security and superior image enhancement performance, making it well-suited for practical applications.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3444926