Key-Free Image Encryption Algorithm Based on Self-Triggered Gaussian Noise Sampling - Gao, Kai; Chang, Chin-Chen; Lin, Chia-Chen (IEEE access, 2024)
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.