Abstract:
The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data.
The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a
diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the
original content of images, producing cipher images that resemble noise, thereby substantiating the
encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by
its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST
STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of
encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that
the encryption process achieves a uniform distribution of pixel values across the encrypted images,
masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated
the success of the encryption technique, showing a substantial reduction in the correlation among
adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized
data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to
secure image data. Additionally, a comparative analysis of correlation coefficients using various
encryption methods on the Lenna image offered insights into the relative effectiveness of different
techniques, emphasizing the importance of method selection based on specific security requirements
and data characteristics.
