Deep Learning Based Digital Image Encryption

As technology has evolved, digital images have become increasingly popular. However, these images often exhibit a high degree of correlation and redundancy among pixels, which creates specific requirements for applications like real-time communication and assessment. Traditional encryption approaches for digital images are inefficient, and the algorithms are difficult. A thorough analysis can highlight existing patterns and provide light on unstudied issues in deep learning and chaotic image encryption. This paper, provides a schematic review of digital image encryption using deep learning architecture. We also proposed a framework that merges the strengths of deep learning and chaotic systems, tackling both computational efficiency and the need for secure encryption. The combination is crucial for pulling intricate patterns from large sets of data and using the chaotic, unpredictable nature of the map to create highly secure encryption keys. The complex behavior of the multi-dimensional chaotic map, along with deep learning model’s ability to extract features from vast data, ensures our generated keys are both random and unpredictable which makes them highly resistant to attempts at pattern finding and reverse engineering, safeguarding the integrity of the encryption.