ABSTRACT In today's digital world, with increasing threats of cyberattacks and unauthorized data access, protecting confidential information demands more robust security mechanisms. Cryptography and steganography are two prominent techniques employed to secure data. However, conventional steganography suffers from reduced embedding capacity and the risk of image distortion. To overcome these challenges, this research proposes a novel hybrid framework to enhance data security and embedding efficiency. The approach comprises two phases including the embedding phase and the extraction phase. In the embedding phase, both the cover and secret images undergo a 3‐level discrete wavelet transform (DWT) using the Daubechies wavelet. Region selection in the transformed cover image is optimized by extracting and leveraging various features, including color, shape, deep features, and local Gabor transitional pattern (LGTrP) features. These features are processed via a modified Bidirectional Long Short‐Term Memory (Bi‐LSTM) model, enhanced with architectural improvements, which boost feature learning. Simultaneously, the secret image undergoes transformation using a modified Arnold map integrated with a Bernoulli map allows faster execution. The modified Arnold function's outcome is subjected to an encryption process. The embedding process is done after the encryption process; the modified Blowfish algorithm is used for the decryption process. Subsequently, the inverse Bernoulli map is utilized, with the resultant output given to the inverse Arnold map. Finally, an inverse 3‐level DWT reconstructs the original secret image. Comparative evaluations demonstrate the proposed framework attains lower KPA and KCA rates of 0.12 and 0.15, respectively, which underscores the innovation of integrating a steganography‐cryptography model in securing sensitive data against sophisticated attacks.
Reddy et al. (Fri,) studied this question.